Prof. Bin Zheng Profile

Prof. Bin Zheng

Professor at Univ of Oklahoma

SPIE Involvement:

Senior status | Author

Area of Expertise:

Computer-aided detection and diagnosis , Machine learning , Technology assessment , Medical image processing

Websites:

Company Website

Profile Summary

I received a Bachelor and Master degree of Engineering in the major of Optical Engineering and Instrumentation from Shanghai Institute of Mechanical Engineering, China, and a doctoral degree of Electrical Engineering from University of Delaware, USA, in 1993.

During 1993 to 2013, I have been working in the Medical Imaging Research Center, University of Pittsburgh. My research interest and work focused on the development and evaluation of computer-aided detection and diagnosis (CAD) schemes of medical images that include mammograms and magnetic resonance image (MRI) for breast cancer, computed tomography (CT) images for lung diseases, and microscopic cytogenetic images for leukemia and cervical cancer. After joining the faculty in University of Oklahoma in May 2013, I continue my research work in the fields of developing and optimizing interactive CAD schemes that aim to provide clinicians “visual-aided” tools in cancer diagnosis, developing and validating the computerized biomarkers extracted from the biomedical images and electrical signals in order to help improve accuracy and reliability of assessing cancer prognosis and treatment efficacy.

Publications (151)

Proceedings Article | 15 March 2019

Assessment of short-term breast cancer risk using a frequency domain correlation based imaging marker

Morteza Heidari, Alan Hollingsworth, Seyedehnafiseh Mirniaharikandehei, Gopichandh Danala, Yuchen Qiu, Hong Liu, Bin Zheng

Proc. SPIE. 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Tumor growth modeling, Cancer, Breast cancer, Feature extraction, Mammography, Machine learning, Performance modeling

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Proceedings Article | 15 March 2019

Developing a quantitative ultrasound image feature analysis scheme to assess tumor treatment efficacy using a mouse model

Seyedehnafiseh Mirniaharikandehei, Joshua VanOsdol, Morteza Heidari, Gopichandh Danala, Ashish Ranjan, Bin Zheng

Proc. SPIE. 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography

KEYWORDS: Image fusion, Computer aided diagnosis and therapy, Cancer, Tumors, Ultrasonography, Image segmentation, Feature extraction, Image analysis, Computer aided design, Mouse models

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Proceedings Article | 15 March 2019

Design, fabrication and evaluation of non-imaging, label-free pre-screening tool using quantified bio-electrical tissue profile

Ali Zarafshani, Yunzhi Wang, Seyedeh-Nafiseh Mirniaharikandehei, Morteza Heidari, Faranak Aghaei, Siqi Wang, Liangzhong Xiang, Bin Zheng

Proc. SPIE. 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Breast, Cancer, Breast cancer, Tissues, Feature extraction, Mathematics, Mammography, Machine learning, Computer aided design, Tissue optics

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The design, fabrication, and performance of pre-screening tool using bioelectrical tissue profile is described. A probe of 8 electrodes using active-probe-sensing (AP-sensing) module was linearity distortion in the frequency response bonded to the 8 sub-regions of the breast surface forming the Bioimpedance transfer measurement system. Each measurement channel acquired the data from 1/8 of breast according to each breast defined in 8 sub-regions with split current injection channel and response behavioural bioelectricity detection. For this tissue bioelectrical measurement system, the electrodes were placed on the breast surface and before the bioelectrical profile were measured, we investigated a closed loop technique to compensate for the effects and measure the channel-skin-contact impedance. The AP-sensing module and connecting pads could be placed in the measurement electrode-Bras according to different breast size, and all measurement sub-regions should be equivalent for each case, but could be different in scale of 1- 2cm in related to different breast size. Bilateral structure was applied to compare each breast tissue bioelectrical properties in related to tissue behavioural in different frequency. Bioelectrical measurement efficiency was evaluated by the use of bioelectrical plots equivalent to a theoretical plot of the pure tissue profile versus average intracellular and extracellular and admittance behavioural of breast tissue able to flow electric field using Cole-Cole structure tissue modelling as a well-known bioelectrical tissue profile. The bioelectrical tissue profile as a pre-screening tool using theoretical pure tissue profiles and experimental measurements were evaluated in related a conventional Bioimpedance spectroscopy instruments. Breast bioelectrical profile of different breast density categories and average measurement values were significant, according to exist of fibro-glandular tissues in the total breast volume. The different of the theoretical values corresponding to pure fatty and fibro-glandular breast tissue behaviour was slightly different with the experimental measurements. We demonstrated the bioelectrical profile of breast tissue and extracting bioelectrical features that comparing in a bilateral structure to apply bioelectrical features as a supplementary data in the machine learning algorithms and present correspond risk factor for susceptibility of breast cancer in future studies. The preliminary equivalent theoretical and experimental results, evaluate the possibility of this new, non-imaging and label-free quantitative technique.

Proceedings Article | 15 March 2019

Electroacoustic tomography (EAT): linear scanning with a single element transducer

Ali Zarafshani, John Merrill, Siqi Wang, Mengxiao Wang, Bin Zheng, Liangzhong Xiang

Proc. SPIE. 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography

KEYWORDS: Real time imaging, Tissues, Ultrasonography, Electrodes, Ultrasonics, Tomography, Transducers, Signal processing, Acoustic emission, Acoustics, Signal detection

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Proceedings Article | 15 March 2019

Developing a computer-aided image analysis and visualization tool to predict region-specific brain tissue “at risk” for developing acute ischemic stroke

Gopichandh Danala, Morteza Heidari, Faranak Aghaei, Bappaditya Ray, Bin Zheng

Proc. SPIE. 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Visualization, Tissues, Blood, Image segmentation, Image analysis, Blood circulation, Artificial intelligence, Computer aided design, Neuroimaging, Brain

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Proceedings Article | 15 March 2019

Electroacoustic tomography system with nanosecond electric pulse excitation source

Ali Zarafshani, John Merrill, Siqi Wang, Bin Zheng, Liangzhong Xiang

Proc. SPIE. 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography

KEYWORDS: Real time imaging, Tissues, Imaging systems, Electrodes, Ultrasonics, Tomography, Transducers, Signal processing, Acoustic emission, Acoustics, Signal detection

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The new technique for the imaging guidance to real-time monitoring of electroporation-based medical interventions could be based on the electroacoustic tomography (EAT), where the electric field applied for the electroporation process leads to induced acoustic signals based on the flow of electrical current inside the target conductive tissue. A microsecond to nanosecond electric-pulse (𝜇𝑠 − 𝑛𝑠𝐸𝑃) excitation source is an essential part of this new imaging guided to real-time monitoring electroporation process. This paper presents the design, configuration, and measurement of a compact, low-cost high voltage MOSFET-based pulsed excitation source and the simple structure of the EAT system with the single channel ultrasonic transducer to acquire acoustic signals and complemented by experimentation of its function based on agar-conductive phantom studies. The high-voltage pulsed excitation source has variable pulse widths ranging from 100 𝑛𝑠 𝑡𝑜 10 𝜇𝑠 electric pulse with a variable pulse potential magnitude of up to 1200 Volts (V). The high-voltage 𝜇𝑠 − 𝑛𝑠𝐸𝑃 is powered from a variable input source of 11.3 to 16 V in direct current (DC) and a power controller using a 0 V to 5 V in DC power line so that it is able to provide 0 to full output in potential magnitude. The high-intensity, ultra-short pulsed electric field is then connected to two electrodes separated by a distance (d) where 𝑑 = 1500𝜇𝑚 𝑡𝑜 3400 𝜇 mounted into the conductive media. An unfocused ultrasound transducer with central frequency of 500 kHz is used to acquire real-time acoustic signals. Various conductive media, including two agar-based phantoms with conductivity of 1 𝜇𝑆/𝑐𝑚 , 34 𝑚𝑆/𝑐𝑚 were studied using this pulsed excitation source to induce corresponding acoustic signals. Results indicate feasibility of the enhancing the EAT system that used up to 8 kV/cm, 𝜇𝑠 𝑡𝑜 𝑛𝑠 pulsed excitation source used in the electroporation-based clinical processes enhancing the EAT system as an imaging guidance to real-time, in-situ monitoring for the electroporation-based techniques.

Proceedings Article | 13 March 2019

Association of computer-aided detection results and breast cancer risk

Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala, Wei Qian, Yuchen Qiu, Bin Zheng

Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Cancer, Breast cancer, Image processing, Mammography, Computer aided design, Computer-aided diagnosis, Solid modeling

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Proceedings Article | 13 March 2019

Developing a new quantitative imaging marker to predict pathological complete response to neoadjuvant chemotherapy

Faranak Aghaei, Alan Hollingsworth, Seyedehnafiseh Mirniaharikandehei, Yunzhi Wang, Hong Liu, Bin Zheng

Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis

KEYWORDS: Breast, Principal component analysis, Computer aided diagnosis and therapy, Cancer, Breast cancer, Surgery, Magnetic resonance imaging, Image segmentation, Computer aided design

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Proceedings Article | 7 March 2019

Utilizing deep learning technology to develop a novel CT image marker for categorizing cervical cancer patients at early stage

Wei Liu, Abolfazl Zargaria, Theresa Thai, Tara Castellano, Camille Gunderson, Kathleen Moore, Robert Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

Proc. SPIE. 10879, Biophotonics and Immune Responses XIV

KEYWORDS: Pattern recognition, Computed tomography, Cervical cancer

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Proceedings Article | 4 March 2019

Assessment of a quantitative mammographic imaging marker for breast cancer risk prediction

Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Wei Qian, Yuchen Qiu, Bin Zheng

Proc. SPIE. 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Tumor growth modeling, Cancer, Breast cancer, Mammography, Computer aided design, Performance modeling, Solid modeling

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Proceedings Article | 9 March 2018

Real-time in-situ monitoring of electrotherapy process using electric pulse-induced acoustic tomography (EpAT)

Ali Zarafshani, Nicklas Dang, Pratik Samant, Rowzat Faiz, Bin Zheng, Liangzhong Xiang

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

KEYWORDS: Ultrasonography, Electrodes, Receivers, Ultrasonics, Tomography, Transducers, Acoustic emission, Acoustics, Signal detection, Prototyping

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Proceedings Article | 9 March 2018

Laboratory designs and validations of a glandularity-adjustable dual-purpose breast tissue phantom

Di Wu, Yong Chen, Farid Omoumi, Yuran Guo, Yuhua Li, Bin Zheng, Hong Liu

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

KEYWORDS: Breast, Tissues, Ultrasonography, X-rays, Diagnostics, Physics, Monte Carlo methods, Modeling and simulation, Mass attenuation coefficient

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Proceedings Article | 9 March 2018

Developing a unique portable device to non-invasively detect bio-electrochemical characteristics of human tissues

Ali Zarafshani, Sreeram Dhurjaty, Seyedeh-nafiseh Mirniaharikandehei, Faranak Aghaei, Liangzhong Xiang, Bin Zheng

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

KEYWORDS: Breast, Cancer, Breast cancer, Tissues, Calibration, Electrodes, Skin, Mammography, Bismuth, Signal detection

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Proceedings Article | 6 March 2018

A performance comparison of low- and high-level features learned by deep convolutional neural networks in epithelium and stroma classification

Yue Du, Roy Zhang, Abolfazl Zargari, Theresa Thai, Camille Gunderson, Katherine Moxley, Hong Liu, Bin Zheng, Yuchen Qiu

Proc. SPIE. 10581, Medical Imaging 2018: Digital Pathology

KEYWORDS: Cancer, Breast cancer, Convolutional neural networks, Tissues, Pathology, Feature extraction, Image classification

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Proceedings Article | 6 March 2018

A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

Yunzhi Wang, Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Jing Gong, Wei Qian, Yuchen Qiu, Bin Zheng

Proc. SPIE. 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Computer aided diagnosis and therapy, Image segmentation, Feature extraction, Mammography, Machine learning, Computer aided design, CAD systems, RGB color model, Imaging informatics

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Proceedings Article | 6 March 2018

Association between background parenchymal enhancement of breast MRI and BIRADS rating change in the subsequent screening

Faranak Aghaei, Seyedehnafiseh Mirniaharikandehei, Alan Hollingsworth, Rebecca Stoug, Melanie Pearce, Hong Liu, Bin Zheng

Proc. SPIE. 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Magnetic resonance imaging, Image segmentation, Diagnostics, Machine learning, Image enhancement, Computer-aided diagnosis

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Although breast magnetic resonance imaging (MRI) has been used as a breast cancer screening modality for high-risk women, its cancer detection yield remains low (i.e., ≤ 3&percnt;). Thus, increasing breast MRI screening efficacy and cancer detection yield is an important clinical issue in breast cancer screening. In this study, we investigated association between the background parenchymal enhancement (BPE) of breast MRI and the change of diagnostic (BIRADS) status in the next subsequent breast MRI screening. A dataset with 65 breast MRI screening cases was retrospectively assembled. All cases were rated BIRADS-2 (benign findings). In the subsequent screening, 4 cases were malignant (BIRADS-6), 48 remained BIRADS-2 and 13 were downgraded to negative (BIRADS-1). A computer-aided detection scheme was applied to process images of the first set of breast MRI screening. Total of 33 features were computed including texture feature and global BPE features. Texture features were computed from either a gray-level co-occurrence matrix or a gray level run length matrix. Ten global BPE features were also initially computed from two breast regions and bilateral difference between the left and right breasts. Box-plot based analysis shows positive association between texture features and BIRADS rating levels in the second screening. Furthermore, a logistic regression model was built using optimal features selected by a CFS based feature selection method. Using a leave-one-case-out based cross-validation method, classification yielded an overall 75&percnt; accuracy in predicting the improvement (or downgrade) of diagnostic status (to BIRAD-1) in the subsequent breast MRI screening. This study demonstrated potential of developing a new quantitative imaging marker to predict diagnostic status change in the short-term, which may help eliminate a high fraction of unnecessary repeated breast MRI screenings and increase the cancer detection yield.

Proceedings Article | 6 March 2018

Applying a machine learning model using a locally preserving projection based feature regeneration algorithm to predict breast cancer risk

Morteza Heidari, Abolfazl Zargari Khuzani, Gopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Wei Qian, Bin Zheng

Proc. SPIE. 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Data modeling, Tissues, Image segmentation, Feature extraction, Mammography, Machine learning

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Proceedings Article | 27 February 2018

Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

Morteza Heidari, Abolfazl Zargari Khuzani, Gopichandh Danala, Yuchen Qiu , Bin Zheng

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Tissues, Image segmentation, Feature extraction, Mammography, X-ray technology, Solid modeling

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Proceedings Article | 27 February 2018

Applying a new mammographic imaging marker to predict breast cancer risk

Faranak Aghaei, Gopichandh Danala, Alan Hollingsworth, Rebecca Stoug, Melanie Pearce, Hong Liu, Bin Zheng

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Chest, Computer aided diagnosis and therapy, Tumor growth modeling, Cancer, Breast cancer, Tissues, Image segmentation, Mammography

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Proceedings Article | 27 February 2018

Association between mammogram density and background parenchymal enhancement of breast MRI

Faranak Aghaei, Gopichandh Danala, Yunzhi Wang, Ali Zarafshani, Wei Qian, Hong Liu, Bin Zheng

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Magnetic resonance imaging, Image segmentation, Mammography, Machine learning, Image enhancement

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Proceedings Article | 27 February 2018

Applying a CAD-generated imaging marker to assess short-term breast cancer risk

Seyedehnafiseh Mirniaharikandehei, Ali Zarafshani, Morteza Heidari, Yunzhi Wang, Faranak Aghaei, Bin Zheng

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Cancer, Breast cancer, Mammography, Machine learning, Computer aided design, Performance modeling, Computer-aided diagnosis

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Proceedings Article | 27 February 2018

Applying a new unequally weighted feature fusion method to improve CAD performance of classifying breast lesions

Abolfazl Zargari Khuzani, Gopichandh Danala, Morteza Heidari, Yue Du, Najmeh Mashhadi, Yuchen Qiu, Bin Zheng

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Breast cancer, Tumors, Image segmentation, Feature extraction, Mammography, Computer aided design, Optimization (mathematics)

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Proceedings Article | 27 February 2018

Computer-aided classification of breast masses using contrast-enhanced digital mammograms

Gopichandh Danala, Faranak Aghaei, Morteza Heidari, Teresa Wu, Bhavika Patel, Bin Zheng

Proc. SPIE. 10575, Medical Imaging 2018: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Tumors, Tissues, Image segmentation, Image acquisition, Mammography, Image classification, Image contrast enhancement, Computer aided design, Digital mammography

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Proceedings Article | 19 February 2018

Conventional and phase contrast x-ray imaging techniques and ultrasound imaging method in breast tumor detection: initial comparison studies using phantom

Yuran Guo, Di Wu, Farid Omoumi, Yuhua Li, Molly Donovan Wong, Muhammad Ghani, Bin Zheng, Hong Liu

Proc. SPIE. 10495, Biophotonics and Immune Responses XIII

KEYWORDS: Breast, Phase contrast, Breast cancer, Tissues, Imaging systems, Ultrasonography, X-rays, Mammography, X-ray imaging, Prototyping

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Proceedings Article | 19 February 2018

Real-time, in situ monitoring of nanoporation using electric field-induced acoustic signal

Ali Zarafshani, Rowzat Faiz, Pratik Samant, Bin Zheng, Liangzhong Xiang

Proc. SPIE. 10495, Biophotonics and Immune Responses XIII

KEYWORDS: Real time imaging, Cancer, Tumors, Tissues, Ultrasonography, Electrodes, Magnetic resonance imaging, Transducers, Signal processing, Acoustics

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Proceedings Article | 19 February 2018

Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

Abolfazl Zargari, Yue Du, Theresa Thai, Camille Gunderson, Kathleen Moore, Robert Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

Proc. SPIE. 10495, Biophotonics and Immune Responses XIII

KEYWORDS: Tumors, Image analysis, Computed tomography, Ovarian cancer

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Proceedings Article | 13 March 2017

Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography

Yane Li, Ming Fan, Lihua Li, Bin Zheng

Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Tissues, Databases, Image segmentation, Feature extraction, Mammography, Diagnostics and therapeutics, Spherical lenses, Nipple, Imaging informatics

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Proceedings Article | 13 March 2017

Implementation of a computer-aided detection tool for quantification of intracranial radiologic markers on brain CT images

Faranak Aghaei, Stephen Ross, Yunzhi Wang, Dee Wu, Benjamin Cornwell, Bappaditya Ray, Bin Zheng

Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Image processing algorithms and systems, Computer aided diagnosis and therapy, Visualization, Tissues, Blood, Image segmentation, Image processing, Computed tomography, Skull, Computer-aided diagnosis, Brain

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Proceedings Article | 13 March 2017

Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

Guolin Wu, Ming Fan, Juan Zhang, Bin Zheng, Lihua Li

Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Medicine, Statistical analysis, Cancer, Breast cancer, Tumors, Magnetic resonance imaging, Image segmentation, Chromium, Feature extraction, Diagnostics and therapeutics, Statistical modeling, Performance modeling, Imaging informatics

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Proceedings Article | 13 March 2017

Exploring a new quantitative image marker to assess benefit of chemotherapy to ovarian cancer patients

Seyedehnafiseh Mirniaharikandehei, Omkar Patil, Faranak Aghaei, Yunzhi Wang, Bin Zheng

Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Medicine, Oncology, Computer aided diagnosis and therapy, Cancer, Tissues, Signal attenuation, Databases, Image segmentation, Computed tomography, Spine, Computer-aided diagnosis, Pancreatic cancer, Ovarian cancer, Imaging informatics, Clinical trials

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Proceedings Article | 13 March 2017

Computerized detection of breast cancer using resonance-frequency-based electrical impedance spectroscopy

Wei Gao, Ming Fan, Weijie Zhao, Bin Zheng, Lihua Li

Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Breast, Breast cancer, Tumors, Sensors, Computing systems, Control systems, Feature extraction, Biopsy, Dielectric spectroscopy, Mechanical engineering, Control systems design, Nipple, Evolutionary algorithms, Imaging informatics

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This study developed and tested a multi-probe resonance-frequency-based electrical impedance spectroscopy (REIS) system aimed at detection of breast cancer. The REIS system consists of specially designed mechanical supporting device that can be easily lifted to fit women of different height, a seven probe sensor cup, and a computer providing software for system control and management. The sensor cup includes one central probe for direct contact with the nipple, and other six probes uniformly distributed at a distance of 35mm away from the center probe to enable contact with breast skin surface. It takes about 18 seconds for this system to complete a data acquisition process. We utilized this system for examination of breast cancer, collecting a dataset of 289 cases including biopsy verified 74 malignant and 215 benign tumors. After that, 23 REIS based features, including seven frequency, fifteen magnitude features were extracted, and an age feature. To reduce redundancy we selected 6 features using the evolutionary algorithm for classification. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. A multivariable logistic regression method was performed for detection of the tumors. The results of our study showed for the 23 REIS features AUC and ACC, Sensitivity and Specificity of 0.796, 0.727, 0.731 and 0.726, respectively. The AUC and ACC, Sensitivity and Specificity for the 6 REIS features of 0.840, 0.80, 0.703 and 0.833, respectively, and AUC of 0.662 and 0.619 for the frequency and magnitude based REIS features, respectively. The performance of the classifiers using all the 6 features was significantly better than solely using magnitude features (p=3.29e-08) and frequency features (5.61e-07). Smote algorithm was used to expand small samples to balance the dataset, the AUC after data balance of 0.846，increased than the original data classification performance. The results indicated that the REIS system is a promising tool for detection of breast cancer and may be acceptable for clinical implementation.

Proceedings Article | 13 March 2017

Current source enhancements in Electrical Impedance Spectroscopy (EIS) to cancel unwanted capacitive effects

Ali Zarafshani, Thomas Bach, Chris Chatwin, Liangzhong Xiang, Bin Zheng

Proc. SPIE. 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Breast, Biomedical optics, Cancer, Imaging systems, Electrodes, Resistance, Amplifiers, Data acquisition, Capacitance, Molecular spectroscopy, Resistors, Dielectric spectroscopy, Signal detection, Inductance, Voltage controlled current source

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Proceedings Article | 10 March 2017

Developing a visual sensitive image features based CAD scheme to assist classification of mammographic masses

Yunzhi Wang, Faranak Aghaei, Maxine Tan, Yuchen Qiu, Hong Liu, Bin Zheng

Proc. SPIE. 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Image processing algorithms and systems, Image fusion, Image visualization, Computer aided diagnosis and therapy, Visualization, Image segmentation, Feature extraction, Mammography, Image classification, Computer aided design

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Proceedings Article | 10 March 2017

Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

Faranak Aghaei, Seyedehnafiseh Mirniaharikandehei, Alan Hollingsworth, Yunzhi Wang, Yuchen Qiu, Hong Liu, Bin Zheng

Proc. SPIE. 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Image compression, Computer aided diagnosis and therapy, Tumor growth modeling, Cancer, Breast cancer, Data modeling, Tissues, Image segmentation, Image processing, Mammography, Computer aided design, Performance modeling, Process modeling

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Proceedings Article | 3 March 2017

Automated detection and quantification of residual brain tumor using an interactive computer-aided detection scheme

Kevin Gaffney, Faranak Aghaei, James Battiste, Bin Zheng

Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis

KEYWORDS: Computer aided diagnosis and therapy, Tumors, Tissues, Surgery, Magnetic resonance imaging, Image segmentation, Image processing, Brain cancer, Radiotherapy, Neuroimaging, Computer-aided diagnosis, Brain

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Proceedings Article | 3 March 2017

Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study

Gopichandh Danala, Yunzhi Wang, Theresa Thai, Camille Gunderson, Katherine Moxley, Kathleen Moore, Robert Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis

KEYWORDS: Image fusion, Computer aided diagnosis and therapy, Cancer, Tumors, Wavelets, Medical imaging, Computed tomography, Ovarian cancer

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Proceedings Article | 3 March 2017

Balance the nodule shape and surroundings: a new multichannel image based convolutional neural network scheme on lung nodule diagnosis

Wenqing Sun, Bin Zheng, Xia Huang, Wei Qian

Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis

KEYWORDS: Convolutional neural networks, Visualization, Databases, Image segmentation, Lung, Medical imaging, Computer vision technology, Volume rendering, Machine vision, Neural networks, Machine learning, Computed tomography

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Proceedings Article | 3 March 2017

Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images

Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen Moore, Hong Liu, Bin Zheng

Proc. SPIE. 10134, Medical Imaging 2017: Computer-Aided Diagnosis

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Convolutional neural networks, Tissues, Image segmentation, Image processing, Abdomen, Computed tomography, Solid modeling

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Proceedings Article | 20 February 2017

Measurements of gold nanoparticle concentration with K-shell x-ray fluorescence spectrum

Di Wu, Molly Wong, Yuhua Li, Muhammad Ghani, Bin Zheng, Wei Chen, Hong Liu

Proc. SPIE. 10065, Biophotonics and Immune Responses XII

KEYWORDS: Gold, Optical filters, Scattering, Nanoparticles, Luminescence, Photons, X-rays, Collimators, X-ray fluorescence spectroscopy, Lead

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Proceedings Article | 20 February 2017

Characteristic performance investigation of a photon counting detector for x-ray fluorescence imaging applications

Liqiang Ren, Muhammad Ghani, Di Wu, Yuhua Li, Wei Chen, Ge Wang, Bin Zheng, Hong Liu

Proc. SPIE. 10065, Biophotonics and Immune Responses XII

KEYWORDS: Photon counting, Sensors, Calibration, Nanoparticles, Luminescence, X-rays, Sensor performance, Collimators, X-ray imaging, X-ray detectors, X-ray fluorescence spectroscopy

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Proceedings Article | 20 February 2017

Detectability comparison of simulated objects within a dense breast phantom using high energy x-ray phase sensitive and conventional imaging systems

Muhammad Ghani, Molly Wong, Di Wu, Bin Zheng, Wei Chen, Laurie Fajardo, Xizeng Wu, Hong Liu

Proc. SPIE. 10065, Biophotonics and Immune Responses XII

KEYWORDS: Breast, Phase contrast, Tumors, Tissues, Imaging systems, Sensors, X-rays, Mammography, X-ray imaging, Prototyping

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Proceedings Article | 20 February 2017

Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy

Gopichandh Danala, Yunzhi Wang, Theresa Thai, Camille Gunderson, Katherine Moxley, Kathleen Moore, Robert Mannel, Samuel Cheng, Hong Liu, Bin Zheng, Yuchen Qiu

Proc. SPIE. 10065, Biophotonics and Immune Responses XII

KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Tumors, Sensors, Image segmentation, Image analysis, Computed tomography, Algorithm development, Computer-aided diagnosis, Ovarian cancer

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Proceedings Article | 30 March 2016

Quantitative imaging of the microbubble concentrations by using an in-line phase contrast tomosynthesis prototype: a preliminary phantom study

Di Wu, Muhammad Ghani, Molly Wong, Yuhua Li, Kai Yang, Wei Chen, Bin Zheng, Hong Liu

Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging

KEYWORDS: Breast, Optical filters, Phase contrast, Tissues, Imaging systems, X-rays, Copper, Aluminum, Neodymium, X-ray imaging, Prototyping, Lead

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Proceedings Article | 30 March 2016

Quantitative comparison of spatial resolution in step-and-shoot and continuous motion digital breast tomosynthesis

Muhammad Ghani, Di Wu, Molly Wong, Liqiang Ren, Bin Zheng, Kai Yang, Xizeng Wu, Hong Liu

Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging

KEYWORDS: Breast, Phase contrast, Spatial frequencies, Sensors, Metals, X-rays, X-ray sources, Mammography, Reconstruction algorithms, Modulation transfer functions, Spatial resolution, X-ray imaging, X-ray detectors, Prototyping, Digital breast tomosynthesis

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Proceedings Article | 29 March 2016

A new paradigm of dielectric relaxation spectroscopy for non-invasive detection of breast abnormalities: a preliminary feasibility analysis

Sreeram Dhurjaty, Yuchen Qiu, Maxine Tan, Wei Qian, Bin Zheng

Proc. SPIE. 9783, Medical Imaging 2016: Physics of Medical Imaging

KEYWORDS: Mathematical modeling, Breast, Cancer, Breast cancer, Tissues, Electrodes, Spectroscopy, Dielectrics, Capacitance, Dielectric spectroscopy, Signal detection, Dielectric relaxation, Dielectric polarization

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Proceedings Article | 25 March 2016

Mass classification in mammography with multi-agent based fusion of human and machine intelligence

Dongdong Xi, Ming Fan, Lihua Li, Juan Zhang, Yanna Shan, Gang Dai, Bin Zheng

Proc. SPIE. 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Databases, Computing systems, Mammography, Radiology, CAD systems, Digital mammography, Classification systems

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Proceedings Article | 25 March 2016

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

Lijuan Huang, Ming Fan, Lihua Li, Juan Zhang, Guoliang Shao, Bin Zheng

Proc. SPIE. 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations

KEYWORDS: Breast, Genetic algorithms, Cancer, Breast cancer, Tumors, Magnetic resonance imaging, Image segmentation, Image processing, Feature extraction, Mammography, Image enhancement

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Proceedings Article | 25 March 2016

Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes

Shijian Wang, Ming Fan, Juan Zhang, Bin Zheng, Xiaojia Wang, Lihua Li

Proc. SPIE. 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations

KEYWORDS: Breast, Radon, Statistical analysis, Cancer, Breast cancer, Magnetic resonance imaging, Image segmentation, Feature extraction, Radium, Resonance enhancement, Imaging informatics

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Proceedings Article | 25 March 2016

Dynamic contrast-enhanced magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer

Juzhong Fu, Ming Fan, Bin Zheng, Guoliang Shao, Juan Zhang, Lihua Li

Proc. SPIE. 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations

KEYWORDS: Breast, Medicine, Statistical analysis, Cancer, Tumors, Magnetic resonance imaging, Image segmentation, Remote sensing, Feature extraction, Image enhancement, Image contrast enhancement, Imaging informatics

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Proceedings Article | 24 March 2016

Computer aided lung cancer diagnosis with deep learning algorithms

Wenqing Sun, Bin Zheng, Wei Qian

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Image processing algorithms and systems, Computer aided diagnosis and therapy, Detection and tracking algorithms, Visualization, Databases, Denoising, Lung, Medical imaging, Neural networks, Computed tomography, Image classification, Lung cancer, Computer aided design

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Proceedings Article | 24 March 2016

A B-spline image registration based CAD scheme to evaluate drug treatment response of ovarian cancer patients

Maxine Tan, Zheng Li, Kathleen Moore, Theresa Thai, Kai Ding, Hong Liu, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Target detection, Oncology, Computer aided diagnosis and therapy, Cancer, Tumors, Visualization, Surgery, Image registration, Lung, Solids, Computed tomography, Ovarian cancer, Clinical trials

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Proceedings Article | 24 March 2016

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen More, Kai Ding, Hong Liu, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Statistical analysis, Computer aided diagnosis and therapy, Data modeling, Image segmentation, Feature extraction, Image analysis, Computed tomography, Brain-machine interfaces, Statistical modeling, Solid modeling, Ovarian cancer, Data analysis

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Proceedings Article | 24 March 2016

Improving the performance of lesion-based computer-aided detection schemes of breast masses using a case-based adaptive cueing method

Maxine Tan, Faranak Aghaei, Yunzhi Wang, Wei Qian, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Databases, Image segmentation, Feature extraction, Mammography, Computer aided design, Computer-aided diagnosis, Digital mammography, Warning signs

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Proceedings Article | 24 March 2016

An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

Yuchen Qiu, Yunzhi Wang, Shiju Yan, Maxine Tan, Samuel Cheng, Hong Liu, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Breast cancer, Feature extraction, Computer vision technology, Machine vision, Mammography, Computer aided design, Solid modeling

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Proceedings Article | 24 March 2016

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

Yuchen Qiu, Shiju Yan, Maxine Tan, Samuel Cheng, Hong Liu, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Image segmentation, Feature extraction, Digital imaging, Mammography, Computer aided design

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Proceedings Article | 24 March 2016

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

Faranak Aghaei, Maxine Tan, Alan Hollingsworth, Bin Zheng, Samuel Cheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Cancer, Breast cancer, Tumors, Magnetic resonance imaging, Image segmentation, Chromium, Image enhancement

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Proceedings Article | 24 March 2016

Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms

Maxine Tan, Faranak Aghaei, Alan Hollingsworth, Rebecca Stough, Hong Liu, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Magnetic resonance imaging, Image analysis, Mammography, Computer-aided diagnosis

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Proceedings Article | 24 March 2016

Applying a radiomics approach to predict prognosis of lung cancer patients

Nastaran Emaminejad, Shiju Yan, Yunzhi Wang, Wei Qian, Yubao Guan, Bin Zheng

Proc. SPIE. 9785, Medical Imaging 2016: Computer-Aided Diagnosis

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Tumors, Surgery, Image segmentation, Image analysis, Lung, Quantitative analysis, Machine learning, Computed tomography, Lung cancer, Computer-aided diagnosis, New and emerging technologies

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Proceedings Article | 9 March 2016

Optimized acquisition time for x-ray fluorescence imaging of gold nanoparticles: a preliminary study using photon counting detector

Liqiang Ren, Di Wu, Yuhua Li, Wei Chen, Bin Zheng, Hong Liu

Proc. SPIE. 9709, Biophotonics and Immune Responses XI

KEYWORDS: Gold, Signal to noise ratio, Photon counting, Nanoparticles, Spectroscopy, X-rays, Data acquisition, Image quality, X-ray detectors, X-ray fluorescence spectroscopy, Spectral data processing

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Proceedings Article | 9 March 2016

Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

Yuchen Qiu, Xianglan Lu, Shiju Yan, Maxine Tan, Samuel Cheng, Shibo Li, Hong Liu, Bin Zheng

Proc. SPIE. 9709, Biophotonics and Immune Responses XI

KEYWORDS: Computer aided diagnosis and therapy, Image segmentation, Microscopy, Feature extraction, Neural networks, Genetics, Convolution, Leukemia, Brain mapping, New and emerging technologies

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Proceedings Article | 20 March 2015

Association of mammographic image feature change and an increasing risk trend of developing breast cancer: an assessment

Maxine Tan, Joseph Leader, Hong Liu, Bin Zheng

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Statistical analysis, Cancer, Breast cancer, Databases, Image segmentation, Digital imaging, Mammography, Feature selection

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Proceedings Article | 20 March 2015

A new breast cancer risk analysis approach using features extracted from multiple sub-regions on bilateral mammograms

Wenqing Sun, Tzu-Liang Tseng, Bin Zheng, Jianying Zhang, Wei Qian

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Breast, Cancer, Breast cancer, Image segmentation, Electroluminescence, Feature extraction, Mammography, Image classification, Feature selection, Biological research

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Proceedings Article | 20 March 2015

A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment

Rohith Reddy Gundreddy, Maxine Tan, Yuchen Qui, Bin Zheng

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Computer aided diagnosis and therapy, Databases, Image segmentation, Image processing, Fourier transforms, Medical imaging, Mammography, Image retrieval, Computer aided design, CAD systems

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Proceedings Article | 20 March 2015

Exploring new quantitative CT image features to improve assessment of lung cancer prognosis

Nastaran Emaminejad, Wei Qian, Yan Kang, Yubao Guan, Fleming Lure, Bin Zheng

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Tumors, Surgery, Image segmentation, Image analysis, Lung, Computed tomography, Genetics, Lung cancer

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Proceedings Article | 20 March 2015

Automated detection of breast tumor in MRI and comparison of kinetic features for assessing tumor response to chemotherapy

Faranak Aghaei, Maxine Tan, Bin Zheng

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Breast cancer, Tumors, Magnetic resonance imaging, Image segmentation, Image processing, Image analysis, Image enhancement

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Proceedings Article | 20 March 2015

A new CAD approach for improving efficacy of cancer screening

Bin Zheng, Wei Qian, Lihua Li, Jiantao Pu, Yan Kang, Fleming Lure, Maxine Tan, Yuchen Qiu

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Tissues, Magnetic resonance imaging, Image analysis, Medical imaging, Mammography, Image enhancement

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Proceedings Article | 20 March 2015

Evaluation of chemotherapy response in ovarian cancer treatment using quantitative CT image biomarkers: a preliminary study

Yuchen Qiu, Maxine Tan, Scott McMeekin, Theresa Thai, Kathleen Moore, Kai Ding, Hong Liu, Bin Zheng

Proc. SPIE. 9414, Medical Imaging 2015: Computer-Aided Diagnosis

KEYWORDS: Edge detection, Oncology, Computer aided diagnosis and therapy, Cancer, Tumors, Image segmentation, Image analysis, Computed tomography, Ovarian cancer, Clinical trials

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Proceedings Article | 19 March 2015

A new application of electrical impedance spectroscopy for measuring glucose metabolism: a phantom study

Sreeram Dhurjaty, Yuchen Qiu, Maxine Tan, Hong Liu, Bin Zheng

Proc. SPIE. 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Glucose, Cancer, Tissues, Blood, Capacitance, Dielectric spectroscopy, Signal detection, Positron emission tomography, Liquids, Mode conditioning cables

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Proceedings Article | 17 March 2015

An automated approach to improve efficacy in detecting residual malignant cancer cell for facilitating prognostic assessment of leukemia: an initial study

Yuchen Qiu, Xianglan Lu, Maxine Tan, Shibo Li, Hong Liu, Bin Zheng

Proc. SPIE. 9420, Medical Imaging 2015: Digital Pathology

KEYWORDS: Microscopes, Visual analytics, Computer aided diagnosis and therapy, Cancer, Visualization, Image processing, Objectives, Computer aided design, Signal detection, Leukemia

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Proceedings Article | 21 March 2014

Fusion of digital breast tomosynthesis images via wavelet synthesis for improved lesion conspicuity

Harishwaran Hariharan, Victor Pomponiu, Bin Zheng, Bruce Whiting, David Gur

Proc. SPIE. 9034, Medical Imaging 2014: Image Processing

KEYWORDS: Breast, Image fusion, Breast cancer, Visualization, Tissues, Wavelets, Discrete wavelet transforms, Digital mammography, 3D image processing, Digital breast tomosynthesis

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Proceedings Article | 20 March 2014

Using undiagnosed data to enhance computerized breast cancer analysis with a three stage data labeling method

Wenqing Sun, Tzu-Liang Tseng, Bin Zheng, Flemin Lure, Teresa Wu, Giulio Francia, Sergio Cabrera, Jianying Zhang, Miguel Vélez-Reyesv, Wei Qian

Proc. SPIE. 9035, Medical Imaging 2014: Computer-Aided Diagnosis

KEYWORDS: Statistical analysis, Computer aided diagnosis and therapy, Cancer, Breast cancer, Solid state lighting, Computing systems, Mammography, Machine learning, Feature selection, Analytical research

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Proceedings Article | 19 March 2014

A new resonance-frequency based electrical impedance spectroscopy and its application in biomedical engineering

Sreeram Dhurjaty, Yuchen Qiu, Maxine Tan, Bin Zheng

Proc. SPIE. 9033, Medical Imaging 2014: Physics of Medical Imaging

KEYWORDS: Breast, Oncology, Cancer, Tumors, Tissues, Calibration, Capacitance, Dielectric spectroscopy, Signal detection, Liquids

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Proceedings Article | 18 March 2014

A new mass classification system derived from multiple features and a trained MLP model

Maxine Tan, Jiantao Pu, Bin Zheng

Proc. SPIE. 9035, Medical Imaging 2014: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Tumor growth modeling, Databases, Image segmentation, Feature extraction, Mammography, Image classification, Solid modeling, Classification systems

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Proceedings Article | 18 March 2014

Improving breast mass detection using histogram of oriented gradients

Victor Pomponiu, Harishwaran Hariharan, Bin Zheng, David Gur

Proc. SPIE. 9035, Medical Imaging 2014: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Breast cancer, Tissues, Computing systems, Medical imaging, Mammography, CAD systems, Classification systems

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Proceedings Article | 11 March 2014

Evaluation of correlation between CT image features and ERCC1 protein expression in assessing lung cancer prognosis

Maxine Tan, Nastaran Emaminejad, Wei Qian, Shenshen Sun, Yan Kang, Yubao Guan, Fleming Lure, Bin Zheng

Proc. SPIE. 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Proteins, Cancer, Image segmentation, Image processing, Lung, Computed tomography, Genetics, Lung cancer, Emphysema, Chronic obstructive pulmonary disease

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Proceedings Article | 27 February 2014

Interphase fluorescence in situ hybridization signal detection by computing intensity variance along the optical axis

Zheng Li, Bin Zheng, Liqiang Ren, Hong Liu

Proc. SPIE. 8944, Biophotonics and Immune Responses IX

KEYWORDS: Microscopes, Statistical analysis, Imaging systems, Image segmentation, Luminescence, Image acquisition, Objectives, Analytical research, Signal detection, 3D image processing

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Proceedings Article | 29 March 2013

Texture-based CT Image analysis of asthma

Harishwaran Hariharan, Sally Wenzel, Bin Zheng, Bruce Whiting, Jiantao Pu, David Gur, J. Ken Leader

Proc. SPIE. 8672, Medical Imaging 2013: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Statistical analysis, Visualization, Image segmentation, Image processing, Image analysis, Lung, Medical imaging, Computed tomography, Analytical research, Pulmonary function tests

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Proceedings Article | 28 March 2013

Prediction of near-term breast cancer risk using a Bayesian belief network

Bin Zheng, Pandiyarajan Ramalingam, Harishwaran Hariharan, Joseph Leader, David Gur

Proc. SPIE. 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Tissues, Databases, Image segmentation, Image processing, Medical imaging, Mammography

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Proceedings Article | 18 March 2013

Association between bilateral asymmetry of kinetic features computed from the DCE-MRI images and breast cancer

Qian Yang, Lihua Li, Juan Zhang, Chengjie Zhang, Bin Zheng

Proc. SPIE. 8670, Medical Imaging 2013: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Databases, Magnetic resonance imaging, Image segmentation, Image processing, Feature extraction, Image enhancement

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Proceedings Article | 6 March 2013

Resonance-frequency based electrical impedance spectroscopy and its detection sensitivity to breast lesions

Sreeram Dhurjaty, Bin Zheng, David Gur

Proc. SPIE. 8668, Medical Imaging 2013: Physics of Medical Imaging

KEYWORDS: Breast, Cancer, Breast cancer, Data modeling, Tissues, Electrodes, Resistance, Capacitance, Dielectric spectroscopy, Signal detection

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Proceedings Article | 1 March 2013

A preliminary investigation: the impact of microscopic condenser on depth of field in cytogenetic imaging

Liqiang Ren, Yuchen Qiu, Zheng Li, Yuhua Li, Bin Zheng, Shibo Li, Wei Chen, Hong Liu

Proc. SPIE. 8580, Dynamics and Fluctuations in Biomedical Photonics X

KEYWORDS: Microscopes, Optical microscopes, Statistical analysis, Imaging systems, Spatial frequencies, Objectives, Shape analysis, Spatial resolution, Health sciences, Prototyping

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Proceedings Article | 21 February 2013

Objective evaluation of the microscopic image sharpness for diagnostic metaphase chromosomes

Yuchen Qiu, Yuhua Li, Liqiang Ren, Wei Chen, Bin Zheng, Shibo Li, Hong Liu

Proc. SPIE. 8582, Biophotonics and Immune Responses VIII

KEYWORDS: Microscopes, Sensors, Microscopy, Diagnostics, Image resolution, Image analysis, Bone, Objectives, Prototyping, Image quality standards

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SPIE Journal Paper | 1 September 2012

Impact of the optical depth of field on cytogenetic image quality

Yuchen Qiu, Yuhua Li, Hong Liu, Xiaodong Chen, Bin Zheng, Shibo Li, Wei Chen

JBO Vol. 17 Issue 9

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SPIE Journal Paper | 4 May 2012

otential clinical impact of three-dimensional visualization for fluorescent in situ hybridization image analysis

Zheng Li, Yuhua Li, Hong Liu, Shibo Li, Roy Zhang, Huimin Tian, Bin Zheng, Wei Chen

JBO Vol. 17 Issue 5

KEYWORDS: 3D image processing, Visualization, Signal detection, 3D visualizations, Leukemia, Visual analytics, Cancer, Lithium, 3D acquisition, Image analysis

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Proceedings Article | 16 April 2012

Classification of CT examinations for COPD visual severity analysis

Jun Tan, Bin Zheng, Xingwei Wang, Jiantao Pu, David Gur, Frank Sciurba, J. Ken Leader

Proc. SPIE. 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Visual analytics, Visualization, Image segmentation, Wavelets, Feature extraction, Lung, Computed tomography, Image classification, Emphysema, Chronic obstructive pulmonary disease

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Proceedings Article | 23 February 2012

Perceptual mass segmentation using eye-tracking and seed-growing

Erting Ke, Wei Liu, Weidong Xu, Lihua Li, Bin Zheng, Juan Zhang, Lingnan Zhang

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Breast cancer, Data modeling, Visualization, Image segmentation, Data acquisition, Mammography, Eye models

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Proceedings Article | 23 February 2012

Multi-instance learning for mass retrieval in digitized mammograms

Pengfei Lu, Wei Liu, Weidong Xu, Lihua Li, Bin Zheng, Juan Zhang, Lingnan Zhang

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Image processing algorithms and systems, Cancer, Breast cancer, Tissues, Databases, Image segmentation, Image processing, Mammography, Image retrieval, Expectation maximization algorithms

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Proceedings Article | 23 February 2012

A multi-scale approach to mass segmentation using graph cuts

Xiangying Wu, Weidong Xu, Lihua Li, Wei Liu, Bin Zheng

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Image processing algorithms and systems, Breast, Energy efficiency, Computer aided diagnosis and therapy, Breast cancer, Image segmentation, Image processing, Gaussian filters, Mammography, Magnetorheological finishing

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Proceedings Article | 23 February 2012

Improving performance of computer-aided detection of pulmonary embolisms by incorporating a new pulmonary vascular-tree segmentation algorithm

Xingwei Wang, XiaoFei Song, Brian Chapman, Bin Zheng

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Image processing algorithms and systems, Computer aided diagnosis and therapy, Detection and tracking algorithms, Image segmentation, Image processing, Lung, Computed tomography, Computer aided design, Algorithm development, 3D image processing

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Proceedings Article | 23 February 2012

Improving CAD performance by fusion of the bilateral mammographic tissue asymmetry information

Xingwei Wang, Lihua Li, Wei Liu, Weidong Xu, Dror Lederman, Bin Zheng

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Breast, Image fusion, Computer aided diagnosis and therapy, Cancer, Breast cancer, Tissues, Image segmentation, Mammography, Computer aided design, Data analysis

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Proceedings Article | 22 February 2012

A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection

Xingwei Wang, Yuhua Li, Hong Liu, Shibo Li, Roy Zhang, Bin Zheng

Proc. SPIE. 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Cancer, Visualization, Image segmentation, Image processing, Luminescence, Image analysis, Signal analysis, Cervical cancer, Computer aided design, Signal detection

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Proceedings Article | 22 February 2012

Assessment of two mammographic density related features in predicting near-term breast cancer risk

Bin Zheng, Jules Sumkin, Margarita Zuley, Xingwei Wang, Amy Klym, David Gur

Proc. SPIE. 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Computer aided diagnosis and therapy, Tumor growth modeling, Cancer, Breast cancer, Tissues, Databases, Image segmentation, Detector development, Mammography

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Proceedings Article | 22 February 2012

Assembly and evaluation of a training module and dataset with feedback for improved interpretation of digital breast tomosynthesis examinations

David Gur, Margarita Zuley, Jules Sumkin, Christiane Hakim, Denise Chough, Linda Lovy, Cynthia Sobran, Durwin Logue, Bin Zheng, Amy Klym

Proc. SPIE. 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Cancer, Breast cancer, Databases, Pathology, Mammography, Radiology, Digital breast tomosynthesis, Architectural distortion, Breast imaging

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Proceedings Article | 22 February 2012

Classification of thyroid nodules using a resonance-frequency-based electrical impedance spectroscopy: progress assessment

Bin Zheng, Mitchell Tublin, Dror Lederman, Amy Klym, Erica Brown, David Gur

Proc. SPIE. 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Neck, Cancer, Surgery, Sensors, Ultrasonography, Computing systems, Diagnostics, Measurement devices, Dielectric spectroscopy, Signal detection

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Proceedings Article | 14 February 2012

An automatic scanning method for high throughput microscopic system to facilitate medical genetic diagnosis: an initial study

Yuchen Qiu, Xiaodong Chen, Zheng Li, Yuhua Li, Wei Chen, Bin Zheng, Shibo Li, Hong Liu

Proc. SPIE. 8224, Biophotonics and Immune Responses VII

KEYWORDS: Surgery, Blood, Image processing, Error analysis, Reliability, Computing systems, Diagnostics, Image acquisition, Image quality, Genetics

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Proceedings Article | 16 March 2011

Lung registration using airway tree morphometry

Jun Tan, Bin Zheng, Sang Park, Jiantao Pu, Sally Wenzel, Joseph Leader

Proc. SPIE. 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Computer aided diagnosis and therapy, Detection and tracking algorithms, Tumors, Image segmentation, Image registration, Lung, Medical imaging, Computed tomography, Analytical research, Motion analysis

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Proceedings Article | 16 March 2011

Quantitative computed tomography of lung parenchyma in patients with emphysema: analysis of higher-density lung regions

Dror Lederman, Joseph Leader, Bin Zheng, Frank Sciurba, Jun Tan, David Gur

Proc. SPIE. 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Photovoltaics, Statistical analysis, X-ray computed tomography, Visualization, Image segmentation, Solar cells, Lung, Computed tomography, Emphysema, Chronic obstructive pulmonary disease

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Proceedings Article | 9 March 2011

Texture-based segmentation and analysis of emphysema depicted on CT images

Jun Tan, Bin Zheng, Xingwei Wang, Dror Lederman, Jiantao Pu, Frank Sciurba, David Gur, J. Ken Leader

Proc. SPIE. 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Visualization, Image segmentation, Feature extraction, Lung, Optical inspection, Fractal analysis, Computed tomography, Lung cancer, Emphysema, Chronic obstructive pulmonary disease

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Proceedings Article | 9 March 2011

Multi-probe-based resonance-frequency electrical impedance spectroscopy for detection of suspicious breast lesions: improving performance using partial ROC optimization

Dror Lederman, Bin Zheng, Xingwei Wang, Xiao Hui Wang, David Gur

Proc. SPIE. 7963, Medical Imaging 2011: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Breast cancer, Tissues, Sensors, Biopsy, Mammography, Machine learning, Dielectric spectroscopy, Signal detection

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Proceedings Article | 9 March 2011

Computerized prediction of breast cancer risk: comparison between the global and local bilateral mammographic tissue asymmetry

Xingwei Wang, Dror Lederman, Jun Tan, Xiao Hui Wang, Bin Zheng

Proc. SPIE. 7963, Medical Imaging 2011: Computer-Aided Diagnosis

KEYWORDS: Breast, Tumor growth modeling, Cancer, Breast cancer, Data modeling, Tissues, Databases, Feature extraction, Mammography, Performance modeling

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Proceedings Article | 3 March 2011

Assessment of a CAD scheme in selecting the optimal focused microscopic scanning images of the metaphase chromosomes

Xingwei Wang, Jun Tan, Yuchen Qiu, Yuhua Li, Hong Liu, Shibo Li, Bin Zheng

Proc. SPIE. 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Visualization, Image segmentation, Image processing, Diagnostics, Bone, Objectives, Genetics, Computer aided design

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Proceedings Article | 3 March 2011

Assessing risk of thyroid cancer using resonance-frequency based electrical impedance measurements

Bin Zheng, Mitchell Tublin, Dror Lederman, Amy Klym, Erica Brown, David Gur

Proc. SPIE. 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Neck, Cancer, Breast cancer, Tissues, Surgery, Skin, Diagnostics, Biopsy, Dielectric spectroscopy

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Proceedings Article | 3 March 2011

Fusion of classifiers for REIS-based detection of suspicious breast lesions

Dror Lederman, Xingwei Wang, Bin Zheng, Jules Sumkin, Mitchell Tublin, David Gur

Proc. SPIE. 7966, Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Cancer, Breast cancer, Sensors, Skin, Diagnostics, Feature extraction, Biopsy, Mammography, Machine learning

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Proceedings Article | 11 February 2011

The impact of the depth of field on cytogenetic image quality in scanning microscopy

Yuchen Qiu, Xiaodong Chen, Yuhua Li, Bin Zheng, Shibo Li, Roy Zhang, Wei Chen, Hong Liu

Proc. SPIE. 7900, Biophotonics and Immune Responses VI

KEYWORDS: Statistical analysis, Imaging systems, Lenses, Microscopy, Image quality, Digital imaging, Objectives, Shape analysis, Spatial resolution, Image quality standards

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SPIE Journal Paper | 1 July 2010

Automated identification of abnormal metaphase chromosome cells for the detection of chronic myeloid leukemia using microscopic images

Xingwei Wang, Bin Zheng, Shibo Li, John Mulvihill, Xiaodong Chen, Hong Liu

JBO Vol. 15 Issue 04

KEYWORDS: Image segmentation, Bone, Leukemia, Genetics, Diagnostics, Cancer, Visualization, Image processing, Feature extraction, Chromium

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Proceedings Article | 9 March 2010

Improving CAD performance in pulmonary embolism detection: preliminary investigation

Sang Cheol Park, Brian Chapman, Christopher Deible, Sean Lee, Bin Zheng

Proc. SPIE. 7624, Medical Imaging 2010: Computer-Aided Diagnosis

KEYWORDS: Genetic algorithms, Computer aided diagnosis and therapy, Image segmentation, Arteries, Feature extraction, Lung, Computed tomography, Feature selection, Computer aided design, 3D image processing

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In this preliminary study, a new computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection was developed and tested. The scheme applies multiple steps including lung segmentation, candidate extraction using intensity mask and tobogganing method, feature extraction, false positive reduction using a multifeature based artificial neural network (ANN) and a k-nearest neighbor (KNN) classifier to detect and classify suspicious PE lesions. In particular, a new method to define the surrounding background regions of interest (ROI) depicting PE candidates was proposed and tested in an attempt to reduce the detection of false positive regions. In this study, the authors also investigated following methods to improve CAD performance, which include a grouping and scoring method, feature selection using genetic algorithm, and limitation on allowed suspicious lesions to be cued in one examination. To test the scheme performance, a set of 20 chest CT examinations were selected. Among them, 18 are positive cases depicted 44 verified PE lesions and the remaining 2 were negative cases. The dataset was also divided into a training subset (9 examinations) and a testing subset (11 examinations), respectively. The experimental results showed when applying to the testing dataset CAD scheme using tobogganing method alone achieved 2D region-based sensitivity of 72.1% (220/305) and 3D lesion-based sensitivity of 83.3% (20/24) with total 19,653 2D false-positive (FP) PE regions (1,786.6 per case or approximately 6.3 per CT slice). Applying the proposed new method to improve lung region segmentation and better define the surrounding background ROI, the scheme reduced the region-based sensitivity by 6.5% to 65.6% or lesion-based sensitivity by 4.1% to 79.2% while reducing the FP rate by 65.6% to 6,752 regions (or 613.8 per case). After applying the methods of grouping, the maximum scoring, a genetic algorithm (GA) to delete "redundant" features, and limiting the maximum number of cued-lesions in one examination, CAD scheme further reduced FP rate to 50 per case. Based on the FROC curve, an operating threshold was set up in which the CAD scheme could ultimately achieve 63.2% detection sensitivity with 18.4 FP regions per case when applying to the testing dataset. This study investigated the feasibility of several methods applying to the CAD scheme in detecting PE lesions and demonstrated that CAD performance could depend on many factors including better defining candidate ROI and its background, optimizing the 2D region grouping and scoring methods, selecting the optimal feature set, and limiting the number of allowed cueing lesions per examination.

Proceedings Article | 9 March 2010

Two-dimensional airway analysis using probabilistic neural networks

Jun Tan, Bin Zheng, Sang Cheol Park, Jiantao Pu, Frank Sciurba, Joseph Leader

Proc. SPIE. 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Image processing algorithms and systems, Image segmentation, Lung, Neural networks, Computed tomography, Algorithm development, Radium, Chronic obstructive pulmonary disease, 3D image processing, Evolutionary algorithms

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Proceedings Article | 9 March 2010

Improving performance and reliability of interactive CAD schemes

Xiao-Hui Wang, Sang Cheol Park, Jun Tan, Joseph Leader, Bin Zheng

Proc. SPIE. 7624, Medical Imaging 2010: Computer-Aided Diagnosis

KEYWORDS: Breast, Computer aided diagnosis and therapy, Visualization, Databases, Image segmentation, Reliability, Feature extraction, Medical imaging, Mammography, Computer aided design

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An interactive computer-aided detection or diagnosis (ICAD) scheme allows observers to query suspicious abnormalities (lesions) depicted on medical images. Once a suspicious region is queried, ICAD segments the abnormal region, computes a set of image features, searches for and identifies the reference regions depicted on the verified lesions that are similar to the queried one. Based on the distribution of the selected similar regions, ICAD generates a detection (or classification) score of the queried region depicting true-positive disease. In this study, we assessed the performance and reliability of an ICAD scheme when using a database including total 1500 positive images depicted verified breast masses and 1500 negative images depicted ICAD-cued false-positive regions as well as the leave-one-out testing method. We conducted two experiments. In the first experiment, we tested the relationship between ICAD performance and the size of reference database by systematically increasing the size of reference database from 200 to 3000 images. In the second experiment, we tested the relationship between ICAD performance and the similarity level between the queried image and the retrieved similar references by applying a set of thresholds to systematically remove the queried images whose similarity level to their most "similar" reference images are lower than threshold. The performance was compared based on the areas under ROC curves (AUC). The results showed that (1) as the increase of reference database, AUC value monotonically increased from 0.636±0.041 to 0.854±0.004 and (2) as the increase of similarity threshold values, AUC value also monotonically increased from 0.854±0.004 to 0.932±0.016. The increase of AUC values and the decrease of their standard deviations indicate the improvement of both CAD performance and reliability. The study suggested that (1) assembling the large and diverse reference databases and (2) assessing and reporting the reliability of ICAD-generated results based on the similarity measurement are important in development and application of the ICAD schemes.

Proceedings Article | 24 February 2010

Automated detection of analyzable metaphase chromosome cells depicted on scanned digital microscopic images

Yuchen Qiu, Xingwei Wang, Xiaodong Chen, Yuhua Li, Hong Liu, Shibo Li, Bin Zheng

Proc. SPIE. 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Microscopes, Visual analytics, Computer aided diagnosis and therapy, Visualization, Blood, Diagnostics, Bone, Objectives, Genetics, Computer aided design

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Visually searching for analyzable metaphase chromosome cells under microscopes is quite time-consuming and difficult. To improve detection efficiency, consistency, and diagnostic accuracy, an automated microscopic image scanning system was developed and tested to directly acquire digital images with sufficient spatial resolution for clinical diagnosis. A computer-aided detection (CAD) scheme was also developed and integrated into the image scanning system to search for and detect the regions of interest (ROI) that contain analyzable metaphase chromosome cells in the large volume of scanned images acquired from one specimen. Thus, the cytogeneticists only need to observe and interpret the limited number of ROIs. In this study, the high-resolution microscopic image scanning and CAD performance was investigated and evaluated using nine sets of images scanned from either bone marrow (three) or blood (six) specimens for diagnosis of leukemia. The automated CAD-selection results were compared with the visual selection. In the experiment, the cytogeneticists first visually searched for the analyzable metaphase chromosome cells from specimens under microscopes. The specimens were also automated scanned and followed by applying the CAD scheme to detect and save ROIs containing analyzable cells while deleting the others. The automated selected ROIs were then examined by a panel of three cytogeneticists. From the scanned images, CAD selected more analyzable cells than initially visual examinations of the cytogeneticists in both blood and bone marrow specimens. In general, CAD had higher performance in analyzing blood specimens. Even in three bone marrow specimens, CAD selected 50, 22, 9 ROIs, respectively. Except matching with the initially visual selection of 9, 7, and 5 analyzable cells in these three specimens, the cytogeneticists also selected 41, 15 and 4 new analyzable cells, which were missed in initially visual searching. This experiment showed the feasibility of applying this CAD-guided high-resolution microscopic image scanning system to prescreen and select ROIs that may contain analyzable metaphase chromosome cells. The success and the further improvement of this automated scanning system may have great impact on the future clinical practice in genetic laboratories to detect and diagnose diseases.

Proceedings Article | 24 February 2010

A support vector machine designed to identify breasts at high risk using multi-probe generated REIS signals: a preliminary assessment

David Gur, Bin Zheng, Dror Lederman, Sreeram Dhurjaty, Jules Sumkin, Margarita Zuley

Proc. SPIE. 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Breast cancer, Tissues, Imaging systems, Sensors, Skin, Biopsy, Signal generators, Signal detection, Nipple

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Proceedings Article | 27 March 2009

Pulmonary airways tree segmentation from CT examinations using adaptive volume of interest

Sang Cheol Park, Won Pil Kim, Bin Zheng, Joseph Leader, Jiantao Pu, Jun Tan, David Gur

Proc. SPIE. 7259, Medical Imaging 2009: Image Processing

KEYWORDS: Chest, X-ray computed tomography, Image segmentation, Lung, Bone, Volume rendering, Computed tomography, Bronchoscopy, Chronic obstructive pulmonary disease, 3D image processing

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Proceedings Article | 13 March 2009

A visualization system for CT based pulmonary fissure analysis

Jiantao Pu, Bin Zheng, Sang Cheol Park

Proc. SPIE. 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling

KEYWORDS: Human-machine interfaces, Visual process modeling, Visual analytics, Visualization, Image segmentation, 3D modeling, Lung, Computed tomography, 3D displays, 3D image processing

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Proceedings Article | 13 March 2009

Developing and testing a multi-probe resonance electrical impedance spectroscopy system for detecting breast abnormalities

David Gur, Bin Zheng, Sreeram Dhurjaty, Gene Wolfe, Mary Fradin, Richard Weil, Jules Sumkin, Margarita Zuley

Proc. SPIE. 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Cancer, Breast cancer, Tissues, Sensors, Biopsy, Mammography, Dielectric spectroscopy, Signal detection, Nipple

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In our previous study, we reported on the development and preliminary testing of a prototype resonance electrical impedance spectroscopy (REIS) system with a pair of probes. Although our pilot study on 150 young women ranging from 30 to 50 years old indicated the feasibility of using REIS output sweep signals to classify between the women who had negative examinations and those who would ultimately be recommended for biopsy, the detection sensitivity was relatively low. To improve performance when using REIS technology, we recently developed a new multi-probe based REIS system. The system consists of a sensor module box that can be easily lifted along a vertical support device to fit women of different height. Two user selectable breast placement "cups" with different curvatures are included in the system. Seven probes are mounted on each of the cups on opposing sides of the sensor box. By rotating the sensor box, the technologist can select the detection sensor cup that better fits the breast size of the woman being examined. One probe is mounted in the cup center for direct contact with the nipple and the other six probes are uniformly distributed along an outside circle to enable contact with six points on the outer and inner breast skin surfaces. The outer probes are located at a distance of 60mm away from the center (nipple) probe. The system automatically monitors the quality of the contact between the breast surface and each of the seven probes and data acquisition can only be initiated when adequate contact is confirmed. The measurement time for each breast is approximately 15 seconds during which time the system records 121 REIS signal sweep outputs generated from 200 KHz to 800 KHz at 5 KHz increments for all preselected probe pairs. Currently we are measuring 6 pairs between the center probe and each of six probes located on the outer circle as well as two pairs between probe pairs on the outer circle. This new REIS system has been installed in our clinical breast imaging facility. We are conducting a prospective study to assess performance when using this REIS system under an approved IRB protocol. Over 200 examinations have been conducted to date. Our experience showed that this new REIS system was easy to operate and the REIS examination was fast and considered "comfortable" by examinees since the women presses her breast into the cup herself without any need for forced breast compression, and all but a few highly sensitive women have any sensation of an electrical current during the measurement.

Proceedings Article | 3 March 2009

Computer-aided detection of HER2 amplification status using FISH images: a preliminary study

Bin Zheng, Xiao-Hui Wang, Urvashi Surti, Rohit Bhargava, David Gur

Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis

KEYWORDS: Statistical analysis, Computer aided diagnosis and therapy, Breast cancer, Image segmentation, Luminescence, Image analysis, Genetics, Computer aided design, Signal detection

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The amplification status of human epidermal growth factor receptors 2 (HER2) genes is strongly associated with clinical outcome in patients with breast cancer. The American Society of Clinical Oncology Tumor Marker Guidelines Panel has recommended routine testing of HER2 status on all newly diagnosed metastatic breast cancers since 2001. Although fluorescent in situ hybridization (FISH) technology provides superior accuracy as compared with other approaches, current manual FISH analysis methods are somewhat subjective, tedious, and may introduce interreader variability. The goal of this preliminary study is to develop and test a computer-aided detection (CAD) scheme to assess HER2 status using FISH images. Forty FISH images were selected for this study from our genetic laboratory. The CAD scheme first applies an adaptive, iterative threshold method followed by a labeling algorithm to segment cells of possible interest. A set of classification rules is then used to identify analyzable interphase cells and discard nonanalyzable cells due to cell overlapping and/or other image staining debris (or artifacts). The scheme then maps the detected analyzable cells onto two other gray scale images corresponding to the red and green color of the original image followed by application of a raster scan and labeling algorithms to separately detect the HER-2/neu ("red") and CEP17 ("green") FISH signals. A simple distance based criterion is applied to detect and merge split FISH signals within each cell. The CAD scheme computes the ratio between independent "red" and "green" FISH signals of all analyzable cells identified on an image. If the ratio is ≥ 2.0, the FISH image is assumed to have been acquired from a HER2+ case; otherwise, the FISH image is assumed to have been acquired from HER2- case. When we applied the CAD scheme to the testing dataset, the average computed HER2 amplification ratios were 1.06±0.25 and 2.53±0.81 for HER2- and HER2+ samples, respectively. The results show that the CAD scheme has the ability to automatically detect HER2 status using FISH images. The success of CAD-guided FISH image analysis could result in a more objective, consistent, and efficient approach in determining HER2 status of breast cancers.

SPIE Journal Paper | 1 March 2009

Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens

Xingwei Wang, Bin Zheng, Shibo Li, Roy Zhang, John Mulvihill, Wei Chen, Hong Liu

JBO Vol. 14 Issue 02

KEYWORDS: Cervical cancer, Biological research, Visualization, Luminescence, Image segmentation, Diagnostics, Signal detection, Analytical research, Health sciences, Cancer

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Proceedings Article | 28 February 2009

Association between lung function and airway wall density

J. Ken Leader, Bin Zheng, Carl Fuhrman, John Tedrow, Sang C. Park, Jun Tan, Jiantao Pu, John Drescher, David Gur, Frank Sciurba

Proc. SPIE. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

KEYWORDS: Gold, Medicine, Tissues, Signal attenuation, Image segmentation, Lung, Computed tomography, In vivo imaging, Medium wave, Chronic obstructive pulmonary disease

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Proceedings Article | 12 February 2009

Automated segmentation and analysis of fluorescent in situ hybridization (FISH) signals in interphase nuclei of pap-smear specimens

Xingwei Wang, Bin Zheng, Shibo Li, Roy Zhang, Yuhua Li, John Mulvihill, Wei Chen, Hong Liu

Proc. SPIE. 7176, Dynamics and Fluctuations in Biomedical Photonics VI

KEYWORDS: Image processing algorithms and systems, Statistical analysis, Image segmentation, Luminescence, Diagnostics, Image analysis, Image classification, Cervical cancer, Analytical research, Signal detection

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SPIE Journal Paper | 1 October 2008

Development and assessment of an integrated computer-aided detection scheme for digital microscopic images of metaphase chromosomes

Xingwei Wang, Bin Zheng, Shibo Li, John Mulvihill, Hong Liu

JEI Vol. 17 Issue 04

KEYWORDS: Computer aided design, Databases, Neurons, Genetics, Algorithm development, Visualization, Statistical analysis, Image classification, Diagnostics

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Proceedings Article | 17 March 2008

Assessment of the relationship between lesion segmentation accuracy and computer-aided diagnosis scheme performance

Bin Zheng, Jiantao Pu, Sang Cheol Park, Margarita Zuley, David Gur

Proc. SPIE. 6915, Medical Imaging 2008: Computer-Aided Diagnosis

KEYWORDS: Image processing algorithms and systems, Breast, Computer aided diagnosis and therapy, Detection and tracking algorithms, Tissues, Databases, Image segmentation, Mammography, Computer aided design, Algorithm development

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In this study we randomly select 250 malignant and 250 benign mass regions as a training dataset. The boundary contours of these regions were manually identified and marked. Twelve image features were computed for each region. An artificial neural network (ANN) was trained as a classifier. To select a specific testing dataset, we applied a topographic multi-layer region growth algorithm to detect boundary contours of 1,903 mass regions in an initial pool of testing regions. All processed regions are sorted based on a size difference ratio between manual and automated segmentation. We selected a testing dataset involving 250 malignant and 250 benign mass regions with larger size difference ratios. Using the area under ROC curve (A<sub>Z</sub> value) as performance index we investigated the relationship between the accuracy of mass segmentation and the performance of a computer-aided diagnosis (CAD) scheme. CAD performance degrades as the size difference ratio increases. Then, we developed and tested a hybrid region growth algorithm that combined the topographic region growth with an active contour approach. In this hybrid algorithm, the boundary contour detected by the topographic region growth is used as the initial contour of the active contour algorithm. The algorithm iteratively searches for the optimal region boundaries. A CAD likelihood score of the growth region being a true-positive mass is computed in each iteration. The region growth is automatically terminated once the first maximum CAD score is reached. This hybrid region growth algorithm reduces the size difference ratios between two areas segmented automatically and manually to less than ±15% for all testing regions and the testing A<sub>Z</sub> value increases to from 0.63 to 0.90. The results indicate that CAD performance heavily depends on the accuracy of mass segmentation. In order to achieve robust CAD performance, reducing lesion segmentation error is important.

Proceedings Article | 17 March 2008

Applying a 2D based CAD scheme for detecting micro-calcification clusters using digital breast tomosynthesis images: an assessment

Sang Cheol Park, Bin Zheng, Xiao-Hui Wang, David Gur

Proc. SPIE. 6915, Medical Imaging 2008: Computer-Aided Diagnosis

KEYWORDS: Signal to noise ratio, Breast, Computer aided diagnosis and therapy, Visualization, Image filtering, Mammography, Computer aided design, 3D image processing, Digital breast tomosynthesis

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Digital breast tomosynthesis (DBT) has emerged as a promising imaging modality for screening mammography. However, visually detecting micro-calcification clusters depicted on DBT images is a difficult task. Computer-aided detection (CAD) schemes for detecting micro-calcification clusters depicted on mammograms can achieve high performance and the use of CAD results can assist radiologists in detecting subtle micro-calcification clusters. In this study, we compared the performance of an available 2D based CAD scheme with one that includes a new grouping and scoring method when applied to both projection and reconstructed DBT images. We selected a dataset involving 96 DBT examinations acquired on 45 women. Each DBT image set included 11 low dose projection images and a varying number of reconstructed image slices ranging from 18 to 87. In this dataset 20 true-positive micro-calcification clusters were visually detected on the projection images and 40 were visually detected on the reconstructed images, respectively. We first applied the CAD scheme that was previously developed in our laboratory to the DBT dataset. We then tested a new grouping method that defines an independent cluster by grouping the same cluster detected on different projection or reconstructed images. We then compared four scoring methods to assess the CAD performance. The maximum sensitivity level observed for the different grouping and scoring methods were 70% and 88% for the projection and reconstructed images with a maximum false-positive rate of 4.0 and 15.9 per examination, respectively. This preliminary study demonstrates that (1) among the maximum, the minimum or the average CAD generated scores, using the maximum score of the grouped cluster regions achieved the highest performance level, (2) the histogram based scoring method is reasonably effective in reducing false-positive detections on the projection images but the overall CAD sensitivity is lower due to lower signal-to-noise ratio, and (3) CAD achieved higher sensitivity and higher false-positive rate (per examination) on the reconstructed images. We concluded that without changing the detection threshold or performing pre-filtering to possibly increase detection sensitivity, current CAD schemes developed and optimized for 2D mammograms perform relatively poorly and need to be re-optimized using DBT datasets and new grouping and scoring methods need to be incorporated into the schemes if these are to be used on the DBT examinations.

Proceedings Article | 12 March 2008

The relation of airway size to lung function

J. Ken Leader, Bin Zheng, Frank Sciurba, Carl Fuhrman, Jessica Bon, Sang Park, Jiantao Pu, David Gur

Proc. SPIE. 6916, Medical Imaging 2008: Physiology, Function, and Structure from Medical Images

KEYWORDS: Gold, Visualization, Tissues, Image segmentation, Adaptive optics, Lung, Computed tomography, Spatial resolution, Chronic obstructive pulmonary disease, Spirometry

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Proceedings Article | 6 March 2008

Assembling a prototype resonance electrical impedance spectroscopy system for breast tissue signal detection: preliminary assessment

Jules Sumkin, Bin Zheng, Michelle Gruss, John Drescher, Joseph Leader, Walter Good, Amy Lu, Cathy Cohen, Ratan Shah, Margarita Zuley, David Gur

Proc. SPIE. 6917, Medical Imaging 2008: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Breast cancer, Tissues, Biopsy, Data acquisition, Mammography, Tissue optics, Dielectric spectroscopy, Signal detection, Prototyping

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Using electrical impedance spectroscopy (EIS) technology to detect breast abnormalities in general and cancer in particular has been attracting research interests for decades. Large clinical tests suggest that current EIS systems can achieve high specificity (≥ 90%) at a relatively low sensitivity ranging from 15% to 35%. In this study, we explore a new resonance frequency based electrical impedance spectroscopy (REIS) technology to measure breast tissue EIS signals in vivo, which aims to be more sensitive to small tissue changes. Through collaboration between our imaging research group and a commercial company, a unique prototype REIS system has been assembled and preliminary signal acquisition has commenced. This REIS system has two detection probes mounted in the two ends of a Y-shape support device with probe separation of 60 mm. During REIS measurement, one probe touches the nipple and the other touches to an outer point of the breast. The electronic system continuously generates sweeps of multi-frequency electrical pulses ranging from 100 to 4100 kHz. The maximum electric voltage and the current applied to the probes are 1.5V and 30mA, respectively. Once a "record" command is entered, multi-frequency sweeps are recorded every 12 seconds until the program receives a "stop recording" command. In our imaging center, we have collected REIS measurements from 150 women under an IRB approved protocol. The database includes 58 biopsy cases, 78 screening negative cases, and other "recalled" cases (for additional imaging procedures). We measured eight signal features from the effective REIS sweep of each breast. We applied a multi-feature based artificial neural network (ANN) to classify between "biopsy" and normal "non-biopsy" breasts. The ANN performance is evaluated using a leave-one-out validation method and ROC analysis. We conducted two experiments. The first experiment attempted to classify 58 "biopsy" breasts and 58 "non-biopsy" breasts acquired on 58 women each having one breast recommended for biopsy. The second experiment attempted to classify 58 "biopsy" breasts and 58 negative breasts from the set of screening negative cases. The areas under ROC curves are 0.679 ± 0.033 and 0.606 ± 0.035 for the first and the second experiment, respectively. The preliminary results demonstrate (1) even with this rudimentary system with only one paired probes there is a measurable signal of changes in breast tissue demonstrating the feasibility of applying REIS technology for identifying at least some women with highly suspicious breast abnormalities and (2) the electromagnetic asymmetry between two breasts may be more sensitive in detecting changes in the abnormal breast. To further improve the REIS system performance, we are currently designing a new REIS system with multiple electrical probes and a more sophisticated analysis scheme.

Proceedings Article | 18 February 2008

Using contrast transfer function to evaluate the effect of motion blur on microscope image quality

Marc Wood, Xingwei Wang, Bin Zheng, Shibo Li, Wei Chen, Hong Liu

Proc. SPIE. 6857, Biophotonics and Immune Responses III

KEYWORDS: Microscopes, Cancer, Cameras, Image segmentation, Video, Computing systems, Image quality, Objectives, Modulation transfer functions, Contrast transfer function

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Proceedings Article | 6 February 2008

Development of an integrated computerized scheme for metaphase chromosome image analysis: a robustness experiment

Xingwei Wang, Bin Zheng, Shibo Li, John Mulvihill, Marc Wood, Chaowei Yuan, Wei Chen, Hong Liu

Proc. SPIE. 6855, Complex Dynamics and Fluctuations in Biomedical Photonics V

KEYWORDS: Genetic algorithms, Visual analytics, Visualization, Image analysis, Artificial neural networks, Digital imaging, Genetics, Computer aided design, Neurons

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Our integrated computer-aided detection (CAD) scheme includes three basic modules. The first module detects whether a microscopic digital image depicts a metaphase chromosome cell. If a cell is detected, the scheme will justify whether it is analyzable with a decision tree. Once an analyzable cell is detected, the second module is applied to segment individual chromosomes and to compute two important features. Specifically, the scheme utilizes a modified thinning algorithm to identify the medial axis of a chromosome. By tracking perpendicular lines along the medial axis, the scheme computes four feature profiles, identifies centromeres, and assigns polarities of chromosomes based on a set of pre-optimized rules. The third module is followed to classify chromosomes into 24 types. In this module, each chromosome is initially represented by a vector of 31 features. A two-layer classifier with 8 artificial neural networks (ANN) is optimized by a genetic algorithm. A testing chromosome is first classified into one of the seven groups by the ANN in the first layer. Another ANN is then automatically selected from the seven ANNs in the second layer (one for each group) to further classify this chromosome into one of 24 types. To test the performance and robustness of this CAD scheme, we randomly selected and assembled an independent testing dataset. The dataset contains 100 microscopic digital images including 50 analyzable and 50 un-analyzable metphase cells identified by the experts. The centromere location, the corresponding polarity, and karyotype for each individual chromosome were recorded in the "truth" file. The performance of the CAD scheme applied to this image dataset is analyzed and compared with the results in the true file. The assessment accuracies are 93% for the first module, 90.8% for centromere identification and 93.2% for polarity assignment in the second module, over 96% for six chromosome groups and 81.8% for one group in the third module, respectively. These accuracy levels are very comparable with those achieved during our previous studies to develop and optimize these CAD modules. Hence, the study demonstrates that our automated scheme can achieve high and robust performance in identification and classification of metaphase chromosomes.

Proceedings Article | 30 March 2007

Learning distance metrics for interactive search-assisted diagnosis of mammograms

Liu Yang, Rong Jin, Rahul Sukthankar, Bin Zheng, Lily Mummert, M. Satyanarayanan, Mei Chen, Drazen Jukic

Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis

KEYWORDS: Diamond, Computer aided diagnosis and therapy, Cancer, Feature extraction, Biopsy, Mammography, Machine learning, Image retrieval, Binary data, Expectation maximization algorithms

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Proceedings Article | 30 March 2007

Mass margins spiculations: agreement between ratings by observers and a computer scheme

Bin Zheng, Gordon Abrams, Lara Hardesty, Sang Cheol Park, John Drescher, Glenn Maitz, David Gur

Proc. SPIE. 6514, Medical Imaging 2007: Computer-Aided Diagnosis

KEYWORDS: Image processing algorithms and systems, Breast, Computer aided diagnosis and therapy, Detection and tracking algorithms, Visualization, Tissues, Image segmentation, Mammography, Computer aided design, Binary data

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This study investigated the agreement between breast mass spiculation levels as rated subjectively by observers and a computer scheme. An image dataset with 1,263 mass regions was selected. First, three experienced observers independently rated subjectively the visualized spiculation levels of these mass regions and classified them into three categories (none/minimal, moderate, and severe/significant). We then developed a computerized scheme to detect mass margins and classify the spiculation levels of the suspected mass regions. The scheme applied a hybrid region growth algorithm to segment the mass regions. An edge map was computed inside a 30-pixel-wide band surrounding the mass boundary contour. The scheme then applied a threshold to convert the edge map into a binary image followed by labeling and detecting line orientation. In the original edge map the scheme computed the average local pixel value fluctuation. In the binary edge map, the scheme computed the ratio between the number of "spiculated" pixels and the number of total pixels inside the band. The scheme also computed mass region conspicuity using the original image. Using these three features, a Bayesian Belief Network (BBN) was built to classify mass regions into one of the three spiculation categories. We compared the inter-observer variation as well as agreement levels between the subjective and computerized ratings. Agreement rates between paired observers ranged from 41.3% to 58.8% (Kappa = 0.136 to 0.309). The agreement between the computer scheme and observers' average rating was 49.2% (Kappa = 0.218). This study demonstrated a large inter-observer variability in subjective rating of mass speculation levels as well as a large difference between the rating results of a computerized scheme and observers. As a result, in an Interactive Computer-Aided Diagnosis (ICAD) environment, CAD-selected reference regions may be considered "very similar" by some observers and "not similar" by others. Hence, improving the selection of actually visually similar reference regions by a computerized scheme remains an important yet unsolved task for ICAD development.

Proceedings Article | 29 March 2007

Quantification of airway morphometry: the effect of CT acquisition and reconstruction parameters

J. Ken Leader, Bin Zheng, Frank Sciurba, Harvey Coxson, Carl Fuhrman, Jessica McMurray, Sang Park, Glenn Maitz, David Gur

Proc. SPIE. 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images

KEYWORDS: Medicine, X-ray computed tomography, CT reconstruction, Tissues, Image segmentation, Scanners, Manufacturing, Lung, Computed tomography, Chronic obstructive pulmonary disease

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Proceedings Article | 8 March 2007

Evaluation of an interactive computer-aided diagnosis (ICAD) system for mammography: a pilot study

Bin Zheng, Gordon Abrams, Cynthia Britton, Christiane Hakim, Amy Lu, Ronald Clearfield, John Drescher, Glenn Maitz, David Gur

Proc. SPIE. 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Visualization, Image segmentation, Computing systems, LCDs, Mammography, Computer aided design, Library classification systems

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Five radiologists detected suspicious mass regions depicted on mammograms acquired from 32 examinations during this pilot study. Among these, 24 examinations depicted subtle masses (12 malignant and 12 benign) and 8 were negative. Each observer interpreted a case in a sequential order under three reading modes. In mode one, the observer interpreted images without viewing CAD-generated cues. The observer provided two likelihood scores (for detection and classification) for each identified suspicious region. In mode two, CAD-cued results were provided and the observer could decide whether to make any changes in the previous ratings. In mode three, each observer was forced to query at least one suspected region. Once a region was queried, CAD scheme automatically segmented the mass region and computed a set of image features. Using a conditioned <i>k</i>-nearest neighbor (KNN) algorithm, six reference regions that were considered "the most similar" to the queried region were selected and displayed along with CAD-generated scores. Again, the observer had an option to change previous ratings. Experimental results were analyzed using ROC method. Five observers marked total 271, 276, and 281 mass regions under the three reading modes, respectively. In mode 2 observers marked 5 new suspected mass regions and did not make any changes in previously rated detection or classification scores. In mode three, although observers queried 18 additional regions, 13 were discarded and 5 were marked with region specific related scores. The observers also changed previous rating scores of 28 mass regions marked during mode one. The areas under ROC curves for individual readers ranged from 0.51 to 0.71 for mass detection (<i>p</i> = 0.67) and from 0.50 to 0.73 for mass classification (<i>p</i> = 0.43). This pilot study suggested that using ICAD could increase radiologists' confidence in their decision making. We also found that because radiologists tend to accept a higher false-positive rate in a laboratory environment, once they made their detection decision during the initial reading, they are frequently reluctant to make changes during the following modes. Hence, while simple and efficient operationally, the sequential reading mode may not be an optimal approach to evaluate the actual utility of ICAD.

Proceedings Article | 17 March 2006

Actual versus intended use of CAD systems in the clinical environment

Bin Zheng, Denise Chough, Perrin Ronald, Cathy Cohen, Christiane Hakim, Gordon Abrams, Marie Ganott, Luisa Wallace, Rattan Shah, Jules Sumkin, David Gur

Proc. SPIE. 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Computer aided diagnosis and therapy, Cancer, Glasses, Computing systems, Mammography, Computer aided design, Radiology, CAD systems

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Proceedings Article | 13 March 2006

Airway morphometry in the lungs as depicted in chest CT examinations variability of measurements

J. Leader, Bin Zheng, Frank Scuirba, Harvey Coxson, Joel Weissfeld, Carl Fuhrman, Glenn Maitz, David Gur

Proc. SPIE. 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images

KEYWORDS: Gold, Chest, Visualization, Tissues, Image segmentation, Adaptive optics, Lung, Computed tomography, Emphysema, Chronic obstructive pulmonary disease

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Proceedings Article | 13 March 2006

CT acquisition technique and quantitative analysis of the lung parenchyma: variability and corrections

Bin Zheng, J. Leader, Harvey Coxson, Frank Scuirba, Carl Fuhrman, Arzu Balkan, Joel Weissfeld, Glenn Maitz, David Gur

Proc. SPIE. 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images

KEYWORDS: Gold, Medicine, X-ray computed tomography, CT reconstruction, Visualization, Diagnostics, Lung, Computed tomography, Emphysema, Chronic obstructive pulmonary disease

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Proceedings Article | 10 March 2006

Increasing sensitivity of masses cued on both views by CAD

Bin Zheng, Glen Maitz, Joseph Leader, David Gur

Proc. SPIE. 6144, Medical Imaging 2006: Image Processing

KEYWORDS: Chest, Computer aided diagnosis and therapy, Cancer, Databases, Image processing, Computer aided design, Environmental sensing, CAD systems, Nipple

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Although CAD schemes can detect a high percentage of subtle cancers depicted on "prior" and false-negative cases, radiologists frequently discard most of CAD-cued subtle masses in the clinical environment. As a result of the relatively high false-positive detection rate and the fact that a large number of subtle masses are typically cued only on one view cause radiologists to rely less on (and often ignore) CAD-cued masses. In this study, we present a multi-view based method to increase the number of actual masses that are cued by the CAD scheme on both (ipso-lateral) views and at the same time limiting the overall "case-based" false-positive detection rate. The new scheme includes a traditional single-image based CAD scheme and a multi-view processing module. An image database from 435 examinations (or a total of 1,740 images) consisting 235 examinations depicting a verified malignant mass each and 200 negative examinations was used in this study. The single-image based CAD scheme with a fixed operating threshold (i.e. 0.55) was applied to all images. For each CAD identified region (i.e. with detection score ≥ 0.55), the multi-view processing module defined a matched strip on the corresponding ipso-lateral image and identified all "matched" regions located inside the strip (including those with detection score < 0.55). All matched regions are cued on both views and unmatched regions were discarded. CAD scheme initially detected 172 true masses and 576 false-positive regions. Of the 172 masses, 90 were detected on two views (52%) and 82 were detected only on one view. Of the 576 false-positive detections, only 72 pairs (14%) were considered "matched" and 432 were not, resulting in 504 case-based ("independent") cues. Case-based sensitivity and false-positive rate of the CAD scheme were 73.2% and 1.16 per case. When only matched region pairs were cued, 160 masses (68.1%) and 308 false-positive detections (0.71 per case) were identified on two views. Reducing the original operating threshold from 0.55 to 0.49 resulted in 172 masses and 413 false-positive detections (or 0.95 per case) being cued on two views. By increasing sensitivity through the reduction of the detection threshold only inside the matched strip regions in ipso-lateral images the scheme successfully cued masses on two views by maintaining detection sensitivity while reducing the "case-based" false-positive detection rate.

Proceedings Article | 12 May 2004

Automated detection and classification of interstitial lung diseases from low-dose CT images

Bin Zheng, Joseph Leader, Carl Fuhrman, Frank Sciurba, David Gur

Proc. SPIE. 5370, Medical Imaging 2004: Image Processing

KEYWORDS: Chest, Computer aided diagnosis and therapy, Image segmentation, Lung, Fractal analysis, Computed tomography, Image classification, Lung cancer, Computer aided design, Stochastic processes

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Proceedings Article | 4 May 2004

Classification of breast abnormalities under different detection cueing environments

Bin Zheng, Richard Swensson, Amy Klym, Lara Hardesty, Ratan Shah, Luisa Wallace, Christiane Hakim, David Gur

Proc. SPIE. 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Computer aided diagnosis and therapy, Databases, Diagnostics, Mammography, Image classification, Computer aided design, Environmental sensing, CAD systems

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Proceedings Article | 22 May 2003

Impedance measurements for early detection of breast cancer in younger women: a preliminary assessment

Jules Sumkin, Alexander Stojadinovic, Michelle Huerbin, Amy Klym, Linda McHugh, Cynthia Sobran, J. Ken Leader, Bin Zheng, David Gur

Proc. SPIE. 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Breast, Lithium, Breast cancer, Ultrasonography, Diagnostics, Biopsy, Medical imaging, Signal processing, Mammography, Algorithm development

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Proceedings Article | 15 May 2003

Improving CAD performance in detecting masses depicted on prior images

Bin Zheng, Xiao Hui Wang, Luisa Wallace, Cathy Cohen, Lara Hardesty, Christiane Hakim, Gordon Abran, Jules Sumkin, David Gur

Proc. SPIE. 5032, Medical Imaging 2003: Image Processing

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Databases, Computer simulations, Feature extraction, Biopsy, Artificial neural networks, Mammography, Computer aided design

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We investigated a new approach to improve the performance of a computer-aided detection (CAD) scheme in identifying masses depicted on images acquired earlier ("prior"). The scheme was trained using a dataset with simulated mass features. From a database with images acquired during two consecutive examinations, 100 locations matched pairs of malignant mass regions were selected in both the “current” and the most recent “prior” images. While reviewing the current images, mass regions were identified and as a result biopsies were ultimately performed. Prior images were not identified as suspicious by radiologists during the original interpretation. The same number of false-positive regions was also selected in both current and prior images. The selected regions were then randomly divided into training and testing datasets with 50 true-positive and 50 false-positive regions in each. For each selected region, five features; area, contrast, circularity, normalized standard deviation of radial length, and conspicuity; were computed. The ratios of the average difference of five feature values between current and prior mass regions in the training datasets were also computed. Multiplying these ratios by the computed values in current mass regions, we generated a new dataset of simulated features of “prior” mass regions. Three artificial neural networks (ANN) were trained. ANN-1 and ANN-2 were trained using training datasets of current and prior regions, respectively. ANN-3 was trained using simulated “prior” dataset. The performance of three ANNs was then evaluated using the testing dataset of prior images. Areas under ROC curves (A<sub>z</sub>) were 0.613 ± 0.026 for ANN-1, 0.678 ± 0.029 for ANN-2, and 0.667 ± 0.029 for ANN-3, respectively. This preliminary study demonstrated that one could estimate an average change of feature values over time and "adjust" CAD performance for better detection of masses at an earlier stage.

Proceedings Article | 15 May 2003

A simple method for automated lung segmentation in x-ray CT images

Bin Zheng, J. Ken Leader, Glenn Maitz, Brian Chapman, Carl Fuhrman, Robert Rogers, Frank Sciurba, Andrew Perez, Paul Thompson, Walter Good, David Gur

Proc. SPIE. 5032, Medical Imaging 2003: Image Processing

KEYWORDS: X-ray computed tomography, Visualization, Image segmentation, Image processing, X-rays, Lung, Image filtering, Computed tomography, X-ray imaging, Emphysema

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Proceedings Article | 16 May 2002

Design considerations for a multisite POTS-based telemammography system

John Drescher, Glenn Maitz, J. Ken Leader, Jules Sumkin, William Poller, Herta Klaman, Bin Zheng, David Gur

Proc. SPIE. 4685, Medical Imaging 2002: PACS and Integrated Medical Information Systems: Design and Evaluation

KEYWORDS: Data compression, Image compression, Breast cancer, Image segmentation, Image processing, Data processing, Telecommunications, Image transmission, Mammography, Computer aided design

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Proceedings Article | 9 May 2002

Incorporation of negative regions in a knowledge-based computer-aided detection scheme

Yuan-Hsiang Chang, Xiao Hui Wang, Lara Hardesty, Christiane Hakim, Bin Zheng, Walter Good, David Gur

Proc. SPIE. 4684, Medical Imaging 2002: Image Processing

KEYWORDS: Information fusion, Computer aided diagnosis and therapy, Breast cancer, Tissues, Image processing, Feature extraction, Mammography, Computer aided design, Radiology

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Proceedings Article | 9 May 2002

Change of region conspicuity in bilateral mammograms: potential impact on CAD performance

Bin Zheng, Xiao Hui Wang, Yuan-Hsiang Chang, Lara Hardesty, Marie Ganott, Walter Good, David Gur

Proc. SPIE. 4684, Medical Imaging 2002: Image Processing

KEYWORDS: Breast, Chest, Computer aided diagnosis and therapy, Tissues, Skin, Image registration, Mammography, Computer aided design, Algorithm development, Nipple

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Proceedings Article | 12 April 2002

Detecting abnormalities in noncued areas of digitized mammograms: an observer experience

William Poller, Bin Zheng, Jules Sumkin, David Gur

Proc. SPIE. 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment

KEYWORDS: Chest, Computer aided diagnosis and therapy, Cancer, Diagnostics, Biopsy, Mammography, Computer aided design, Psychology, Radiology, Environmental sensing

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Proceedings Article | 3 July 2001

Computerized analysis of lesions in 3D MR breast images

He Wang, Bin Zheng, Walter Good, Xiao Hui Wang

Proc. SPIE. 4322, Medical Imaging 2001: Image Processing

KEYWORDS: Breast, 3D image enhancement, Breast cancer, Tissues, Magnetic resonance imaging, Image segmentation, Feature extraction, Image registration, Mammography, 3D image processing

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Proceedings Article | 28 May 2001

Correction of digitized mammograms to enhance soft display and tissue composition measurement

Xiao Hui Wang, Bin Zheng, Yuan-Hsiang Chang, Walter Good

Proc. SPIE. 4319, Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures

KEYWORDS: Breast, Image compression, Tissues, Image processing, Skin, Neural networks, Mammography, Image enhancement, Image classification, Image display

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Proceedings Article | 18 April 2000

Image modification for display of temporal sequences of mammograms

Walter Good, Glenn Maitz, John Drescher, Xiao Hui Wang, Bin Zheng, Yuan-Hsiang Chang, He Wang

Proc. SPIE. 3976, Medical Imaging 2000: Image Display and Visualization

KEYWORDS: Breast, Chest, Image compression, Tissues, Sensors, Skin, Image restoration, Image registration, Mammography, Nipple

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Proceedings Article | 21 May 1999

Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection

Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Walter Good, David Gur

Proc. SPIE. 3661, Medical Imaging 1999: Image Processing

KEYWORDS: Genetic algorithms, Computer aided diagnosis and therapy, Breast cancer, Databases, Image segmentation, Image processing, Artificial neural networks, Mammography, Computer networks, Computer aided design

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Proceedings Article | 21 May 1999

Optimizing the feature set for a Bayesian network for breast cancer diagnosis using genetic algorithm techniques

Xiao Hui Wang, Bin Zheng, Yuan-Hsiang Chang, Walter Good

Proc. SPIE. 3661, Medical Imaging 1999: Image Processing

KEYWORDS: Breast, Genetic algorithms, Cancer, Breast cancer, Databases, Image processing, Computing systems, Mammography, Gallium, Testing and analysis

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Proceedings Article | 21 May 1999

Generalized procrustean image deformation for subtraction of mammograms

Walter Good, Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Glenn Maitz

Proc. SPIE. 3661, Medical Imaging 1999: Image Processing

KEYWORDS: Breast, Tissues, Image processing, Skin, Image restoration, Image registration, Mammography, Bismuth, Computer aided design, Nipple

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Proceedings Article | 21 May 1999

Multi-image CAD employing features derived from ipsilateral mammographic views

Walter Good, Bin Zheng, Yuan-Hsiang Chang, Xiao Hui Wang, Glenn Maitz, David Gur

Proc. SPIE. 3661, Medical Imaging 1999: Image Processing

KEYWORDS: Breast, Image compression, Computer aided diagnosis and therapy, Tissues, Image segmentation, Image processing, Mammography, Computer aided design, Algorithm development, Nipple

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Proceedings Article | 24 June 1998

Assessment of mass detection using tissue background information as input to a computer-assisted diagnosis scheme

Bin Zheng, Yuan-Hsiang Chang, Walter Good, David Gur

Proc. SPIE. 3338, Medical Imaging 1998: Image Processing

KEYWORDS: Computer aided diagnosis and therapy, Tissues, Databases, Image processing, Feature extraction, Medical imaging, Artificial neural networks, Mammography, Computer aided design, Neurons

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SPIE Journal Paper | 1 November 1993

Real-time characterization of motion of motile microorganisms by means of a hybrid laser Doppler velocimeter technique

Bin Zheng, Charles Pleass, Charles Ih

OE Vol. 32 Issue 11

KEYWORDS: Microorganisms, Information operations, Laser Doppler velocimetry, Computing systems, Doppler effect, Data processing, Light scattering, Pattern recognition, Particles, Water

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Proceedings Article | 1 August 1990

Reality of holographic optical head

Charles Ih, Rongsheng Tian, Bin Zheng, K. Lu

Proc. SPIE. 1316, Optical Data Storage

KEYWORDS: Monochromatic aberrations, Optical design, Holographic optical elements, Holography, Lenses, Computer generated holography, Head, Aspheric lenses, Spherical lenses, Tolerancing

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Showing 5 of 151 publications

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