Dr. Zhimin Huo Profile

Dr. Zhimin Huo

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Publications (17)

Proceedings Article | 24 March 2014 Paper

Computer-aided detection of malpositioned endotracheal tubes in portable chest radiographs

Zhimin Huo, Hongda Mao, Jane Zhang, Anne-Marie Sykes, Samson Munn, John Wandtke

Proceedings Volume 9035, 90350K (2014) https://doi.org/10.1117/12.2043826

KEYWORDS: Chest imaging, Spine, Chest, Distance measurement, Lung, Image segmentation, Image quality, Computing systems, Computer aided diagnosis and therapy

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Proceedings Article | 11 March 2014 Paper

Bone suppression technique for chest radiographs

Zhimin Huo, Fan Xu, Jane Zhang, Hui Zhao, Susan Hobbs, John Wandtke, Anne-Marie Sykes, Narinder Paul, David Foos

Proceedings Volume 9037, 90370D (2014) https://doi.org/10.1117/12.2043754

KEYWORDS: Lung, Bone, Chest imaging, Chest, Image segmentation, Visualization, Databases, Image processing, Diagnostics, Chromium

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Proceedings Article | 12 March 2010 Paper

On image rendering methods for improved image consistency in PACS environment

Zhimin Huo, Jane Zhang, Huihai Lu, John Wandtke, David Foos

Proceedings Volume 7628, 762810 (2010) https://doi.org/10.1117/12.844621

KEYWORDS: Image processing, Image quality, Lung, Picture Archiving and Communication System, Chest, X-ray imaging, X-rays, Mammography, Control systems, Radiography

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The average workload per full-time equivalent (FTE) radiologist increased by 70% from1991-1992 to 2006- 2007. The increase is mainly due to the increase (34%) in the number of procedures, particularly in 3D imaging procedures. New technologies such as picture archiving and communication systems (PACS) and embodied viewing capability were accredited for an improved workflow environment leading to the increased productivity. However, the need for further workflow improvement is still in demand as the number of procedures continues growing. Advanced and streamlined viewing capability in PACS environment could potentially reduce the reading time, thus further increasing the productivity. With the increasing number of 3D image procedures, radiographic procedures (excluding mammography) have remained their critical roles in screening and diagnosis of various diseases. Although radiographic procedures decreased in shares from 70% to 49.5%, the total number has remained the same from 1991-1992 to 2006- 2007. Inconsistency in image quality for radiographic images has been identified as an area of concern. It affects the ability of clinicians to interpret images effectively and efficiently in areas where diagnosis, for example, in screening mammography and portable chest radiography, requires a comparison of current images with priors. These priors can potentially have different image quality. Variations in image acquisition techniques (x-ray exposure), patient and apparatus positioning, and image processing are the factors attributed to the inconsistency in image quality. The inconsistency in image quality, for example, in contrast may require manual image manipulation (i.e., windowing and leveling) of images to accomplish an optimal comparison to detect the subtle changes. We developed a tone-scale image rendering technique which improves the image consistency of chest images across time and modality. The rendering controls both the global and local contrast for a consistent look. We expect the improvement could reduce the window and level manipulation time required for an optimal comparison of priors and current images, thus improving both the efficiency and effectiveness of image interpretation. This paper presents a technique for improving the consistency of portable chest radiographic images. The technique is based on regions-of-interest (ROIs) to control both the local and global contrast consistency.

Proceedings Article | 17 March 2008 Paper

Computer-aided interpretation of ICU portable chest images: automated detection of endotracheal tubes

Zhimin Huo, Simon Li, Minjie Chen, John Wandtke

Proceedings Volume 6915, 69152J (2008) https://doi.org/10.1117/12.770546

KEYWORDS: Spine, Lung, Chest, Fourier transforms, Chest imaging, Image enhancement, Statistical analysis, Databases, Image processing, Radiography

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In intensive care units (ICU), endotracheal (ET) tubes are inserted to assist patients who may have difficulty breathing. A malpositioned ET tube could lead to a collapsed lung, which is life threatening. The purpose of this study is to develop a new method that automatically detects the positioning of ET tubes on portable chest X-ray images. The method determines a region of interest (ROI) in the image and processes the raw image to provide edge enhancement for further analysis. The search of ET tubes is performed within the ROI. The ROI is determined based upon the analysis of the positions of the detected lung area and the spine in the image. Two feature images are generated: a Haar-like image and an edge image. The Haar-like image is generated by applying a Haar-like template to the raw ROI or the enhanced version of the raw ROI. The edge image is generated by applying a direction-specific edge detector. Both templates are designed to represent the characteristics of the ET tubes. Thresholds are applied to the Haar-like image and the edge image to detect initial tube candidates. Region growing, combined with curve fitting of the initial detected candidates, is performed to detect the entire ET tube. The region growing or "tube growing" is guided by the fitted curve of the initial candidates. Merging of the detected tubes after tube growing is performed to combine the detected broken tubes. Tubes within a predefined space can be merged if they meet a set of criteria. Features, such as width, length of the detected tubes, tube positions relative to the lung and spine, and the statistics from the analysis of the detected tube lines, are extracted to remove the false-positive detections in the images. The method is trained and evaluated on two different databases. Preliminary results show that computer-aided detection of tubes in portable chest X-ray images is promising. It is expected that automated detection of ET tubes could lead to timely detection of malpositioned tubes, thus improve overall patient care.

Proceedings Article | 4 May 2004 Paper

Comparison of nodule characteristics on CT and radiographic images

Zhimin Huo, Melisa Gao, David Yankelevitz, Claudia Henschke, William Kostis, John Wandtke

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.537363

KEYWORDS: Chromium, Computed tomography, X-ray computed tomography, Lung, Chest, Feature extraction, Solids, Visualization, Lung cancer, Radiography

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The purpose of this study is to identify the difference in nodule characteristics manifested on computed topography (CT) and X-ray images and to evaluate the ability of radiographic features to differentiate between benign and malignant nodules, when compared to the features extracted from CT. We collected 79 consecutive computed radiographic (CR) chest images with one or more CT-documented lung nodules. Upon viewing the CT slices, corresponding nodules were localized on CR images by an experienced chest radiologist. Of the 79 CT nodules (19 benign, 60 malignant), 61 (14 benign, 47 malignant) were considered to be definitely visible on the CR, and the rest were considered to be invisible or did not qualify for distinct feature assessment. Eleven nodule features each were visually extracted from CT and CR images. These features were used to characterize the nodule in terms of size, shape, lobulation, spiculation, density, etc. Correlation between the CT and CR features was calculated for the 61 definitely CR-visible nodules. Receiver operating characteristics (ROC) analysis was performed to evaluate the ability of these features in the task of differentiating between benign and malignant nodules. Results showed that CR and CT images agreed well in characterizing nodules in terms of shape, lobulation, spiculation and density features. We found that 40-50% of the cases had same CR and CT ratings and 41-51% of cases were rated by a difference of one between their ratings on CT and CR for shape (3-point scale), lobulation (4-point scale) and speculation (4-point scale) features. Ninety-two percent of the cases had same CT and CR ratings on the density feature. Size yielded a correlation coefficient of 0.84. In the task of differentiating between benign and malignant lung nodules, ROC analysis of individual features yielded an A_z value ranging from 0.52 to 0.77 for the 14 CT features and from 0.52 to 0.75 for the CR features. In addition, we examined the characteristics of the 18 nodules that were excluded from feature analysis. On average, these 18 nodules were smaller in size (15.2 mm measured from CT) than the 61 CR-visible nodules (23.5 mm). We found that CR features agreed reasonably well with CT features and their ability to differentiate between benign and malignant nodules were similar to that of the CT features.

Proceedings Article | 15 May 2003 Paper

Comparison of approaches for risk-modulated CAD

Maryellen Giger, Li Lan, Zhimin Huo, Carl Vyborny, Hui Li

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481395

KEYWORDS: Breast, Feature extraction, Computing systems, Mammography, Computer aided diagnosis and therapy, Databases, Image processing, Breast cancer, Medical imaging, Lithium

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Proceedings Article | 15 May 2003 Paper

Computerized analysis of mammographic parenchymal patterns using fractal analysis

Hui Li, Maryellen Giger, Zhimin Huo, Olufunmilayo Olopade, Michael Chinander, Li Lan, Ioana Bonta

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481359

KEYWORDS: Fractal analysis, Breast cancer, Mammography, Receivers, Image processing, Lithium, Local area networks, Cancer, Breast, Complex systems

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Proceedings Article | 9 May 2002 Paper

Optimizing feature selection across a multimodality database in computerized classification of breast lesions

Karla Horsch, Alfredo Ceballos, Maryellen Giger, Ioana Bonta, Zhimin Huo, Carl Vyborny, Edward Hendrick, Li Lan

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467053

KEYWORDS: Databases, Feature selection, Mammography, Ultrasonography, Breast, Solids, Analytical research, Biopsy, Acoustics, Lithium

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Proceedings Article | 9 May 2002 Paper

Intelligent CAD workstation for breast imaging using similarity to known lesions and multiple visual prompt aids

Maryellen Giger, Zhimin Huo, Carl Vyborny, Li Lan, Ioana Bonta, Karla Horsch, Robert Nishikawa, Ingrid Rosenborough

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467222

KEYWORDS: Mammography, Visualization, Ultrasonography, Breast, Databases, Computer aided diagnosis and therapy, Breast imaging, Human-machine interfaces, Biopsy, Image segmentation

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Proceedings Article | 9 May 2002 Paper

Effect of case mix on feature selection in the computerized classification of mammographic lesions

Zhimin Huo, Maryellen Giger

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467221

KEYWORDS: Databases, Feature selection, Computer aided diagnosis and therapy, Artificial neural networks, Radiology, Feature extraction, Breast, Breast cancer, Neural networks, Image classification

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Proceedings Article | 3 July 2001 Paper

Computer-aided diagnosis of lesions on multimodality images of the breast

Maryellen Giger, Zhimin Huo, Karla Horsch, Edward Hendrick, Luz Venta, Carl Vyborny, Ioana Bonta, Li Lan

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431141

KEYWORDS: Ultrasonography, Mammography, Breast, Feature extraction, Computer aided diagnosis and therapy, Databases, Image segmentation, Acoustics, Signal attenuation, Biopsy

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Proceedings Article | 26 June 2001 Paper

Analysis of computer-aided diagnosis on radiologists' performance using an independent database

Zhimin Huo, Maryellen Giger, Carl Vyborny

Proceedings Volume 4324, (2001) https://doi.org/10.1117/12.431197

KEYWORDS: Databases, Biopsy, Mammography, Statistical analysis, Computer aided diagnosis and therapy, Breast cancer, Computing systems, Biological research, Cancer, Breast

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Proceedings Article | 6 June 2000 Paper

Incorporation of clinical data into a computerized method for the assessment of mammographic breast lesions

Zhimin Huo, Maryellen Giger

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387626

KEYWORDS: Breast cancer, Databases, Artificial neural networks, Mammography, Breast, Feature extraction, Tumor growth modeling, Cancer, Quantization, Image segmentation

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Proceedings Article | 14 April 2000 Paper

Evaluation of an automated segmentation method based on performances of an automated classification method

Zhimin Huo, Maryellen Giger

Proceedings Volume 3981, (2000) https://doi.org/10.1117/12.383111

KEYWORDS: Image segmentation, Feature extraction, Neural networks, Image classification, Mammography, Receivers, Breast, Computer aided diagnosis and therapy, Algorithm development, Medical imaging

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Proceedings Article | 12 May 1995 Paper

Computerized detection of mammographic lesions: performance of artificial neural network with enhanced feature extraction

Matthew Kupinski, Maryellen Giger, Ping Lu, Zhimin Huo

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208759

KEYWORDS: Artificial neural networks, Neural networks, Feature extraction, Mammography, Databases, Image enhancement, Receivers, Pattern recognition, Digital imaging, Breast cancer

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Proceedings Article | 14 September 1993 Paper

Preliminary evaluation of an "intelligent" mammography workstation

Maryellen Giger, Robert Nishikawa, Robert Schmidt, Carl Vyborny, Ping Lu, Yulei Jiang, Zhimin Huo, John Papaioannou, Chris Wu, S. Cox, R. Kunst, Ulrich Bick, Katrina Rosculet

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154559

KEYWORDS: Mammography, Computer aided diagnosis and therapy, Diagnostics, Image analysis, Image processing, Breast, Cancer, Radiology, Digital mammography, Signal processing

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Proceedings Article | 29 July 1993 Paper

Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

Robert Nishikawa, Maryellen Giger, Kunio Doi, Carl Vyborny, Robert Schmidt, Charles Metz, Chris Wu, Fang-Fang Yin, Yulei Jiang, Zhimin Huo, Ping Lu, Wei Zhang, Takahiro Ema, Ulrich Bick, John Papaioannou, Rufus Nagel

Proceedings Volume 1905, (1993) https://doi.org/10.1117/12.148655

KEYWORDS: Mammography, Breast, Image processing, Signal detection, Image segmentation, Image filtering, Computer aided diagnosis and therapy, Radiology, Artificial neural networks, Image enhancement

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Showing 5 of 17 publications

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