Dr. Xingwei Wang Profile

Dr. Xingwei Wang

Research Associate at UPMC Presbyterian

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Author

Publications (17)

Proceedings Article | 16 April 2012 Paper

Classification of CT examinations for COPD visual severity analysis

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

Proceedings Volume 8317, 831723 (2012) https://doi.org/10.1117/12.911751

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

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Proceedings Article | 23 February 2012 Paper

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

Bin Zheng, XiaoFei Song, Brian Chapman, Xingwei Wang

Proceedings Volume 8315, 83152U (2012) https://doi.org/10.1117/12.911301

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

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Proceedings Article | 23 February 2012 Paper

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

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

Proceedings Volume 8315, 831508 (2012) https://doi.org/10.1117/12.910531

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

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Proceedings Article | 22 February 2012 Paper

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

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

Proceedings Volume 8318, 83181C (2012) https://doi.org/10.1117/12.910516

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

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Proceedings Article | 22 February 2012 Paper

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

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

Proceedings Volume 8318, 83181E (2012) https://doi.org/10.1117/12.910630

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

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Proceedings Article | 9 March 2011 Paper

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

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

Proceedings Volume 7965, 79651V (2011) https://doi.org/10.1117/12.878279

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

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Proceedings Article | 9 March 2011 Paper

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

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

Proceedings Volume 7963, 79631Z (2011) https://doi.org/10.1117/12.877380

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

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Proceedings Article | 9 March 2011 Paper

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

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

Proceedings Volume 7963, 79631L (2011) https://doi.org/10.1117/12.876816

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

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Proceedings Article | 3 March 2011 Paper

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

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

Proceedings Volume 7966, 79661C (2011) https://doi.org/10.1117/12.877368

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

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Proceedings Article | 3 March 2011 Paper

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

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

Proceedings Volume 7966, 796618 (2011) https://doi.org/10.1117/12.876864

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

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SPIE Journal Paper | 1 July 2010 Open Access

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, 046026, (July 2010) https://doi.org/10.1117/12.10.1117/1.3476336

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

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Proceedings Article | 24 February 2010 Paper

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

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

Proceedings Volume 7627, 762718 (2010) https://doi.org/10.1117/12.843915

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

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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.

SPIE Journal Paper | 1 March 2009 Open Access

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, 021002, (March 2009) https://doi.org/10.1117/12.10.1117/1.3081545

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

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Proceedings Article | 12 February 2009 Paper

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

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

Proceedings Volume 7176, 717609 (2009) https://doi.org/10.1117/12.813709

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

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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, 043008, (October 2008) https://doi.org/10.1117/12.10.1117/1.3013459

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

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Proceedings Article | 18 February 2008 Paper

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

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

Proceedings Volume 6857, 68570F (2008) https://doi.org/10.1117/12.759203

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

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Proceedings Article | 6 February 2008 Paper

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

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

Proceedings Volume 6855, 685507 (2008) https://doi.org/10.1117/12.761321

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

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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.

Showing 5 of 17 publications

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