Dr. Jiazheng Shi Profile

Dr. Jiazheng Shi

at Michigan Medicine

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Author

Publications (12)

Proceedings Article | 3 March 2009 Paper

A computer-aided diagnosis system for prediction of the probability of malignancy of breast masses on ultrasound images

Jing Cui, Berkman Sahiner, Heang-ping Chan, Jiazheng Shi, Alexis Nees, Chintana Paramagul, Lubomir Hadjiiski

Proceedings Volume 7260, 72600L (2009) https://doi.org/10.1117/12.813722

KEYWORDS: Phase modulation, Computer aided diagnosis and therapy, Lawrencium, Image segmentation, Computing systems, Breast, Classification systems, Ultrasonography, Feature extraction, Image classification

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Proceedings Article | 19 March 2008 Paper

Digital tomosynthesis mammography: improvement of artifact reduction method for high-attenuation objects on reconstructed slices

Jun Ge, Heang-Ping Chan, Berkman Sahiner, Yiheng Zhang, Jun Wei, Lubomir Hadjiiski, Chuan Zhou, Yi-Ta Wu, Jiazheng Shi

Proceedings Volume 6913, 69134O (2008) https://doi.org/10.1117/12.771539

KEYWORDS: Digital mammography, Imaging systems, Modulation transfer functions, Sensors, X-ray sources, X-rays, Breast, Data modeling, Visualization, X-ray imaging

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Proceedings Article | 18 March 2008 Paper

Truncation artifact and boundary artifact reduction in breast tomosynthesis reconstruction

Yiheng Zhang, Heang-Ping Chan, Yi-Ta Wu, Berkman Sahiner, Chuan Zhou, Jun Wei, Jun Ge, Lubomir Hadjiiski, Jiazheng Shi

Proceedings Volume 6913, 69132Y (2008) https://doi.org/10.1117/12.769698

KEYWORDS: Breast, Photovoltaics, Sensors, Image quality, Digital mammography, Tissues, Reconstruction algorithms, Image processing, Signal attenuation, Mammography

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Proceedings Article | 17 March 2008 Paper

Concordance of computer-extracted image features with BI-RADS descriptors for mammographic mass margin

Berkman Sahiner, Lubomir Hadjiiski, Heang-Ping Chan, Chintana Paramagul, Alexis Nees, Mark Helvie, Jiazheng Shi

Proceedings Volume 6915, 69151N (2008) https://doi.org/10.1117/12.770752

KEYWORDS: Mammography, Image segmentation, Feature extraction, Breast, Computer aided diagnosis and therapy, Computing systems, Radiology, Receivers, Image analysis, Medical imaging

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The purpose of this study was to develop and evaluate computer-extracted features for characterizing mammographic mass margins according to BI-RADS spiculated and circumscribed categories. The mass was automatically segmented using an active contour model. A spiculation measure for a pixel on the mass boundary was defined by using the angular difference between the image gradient vector and the normal to the mass, averaged over pixels in a spiculation search region. For the circumscribed margin feature, the angular difference between the principal eigenvector of the Hessian matrix and the normal to the mass was estimated in a band of pixels centered at each point on the boundary, and the feature was extracted from the resulting profile along the boundary. Three MQSA radiologists provided BI-RADS margin ratings for a data set of 198 regions of interest containing breast masses. The features were evaluated with respect to the individual radiologists' characterization using receiver operating characteristic (ROC) analysis, as well as with respect to that from the majority rule, in which a mass was labeled as spiculated (circumscribed) if it was characterized as such by 2 or 3 radiologists, and non-spiculated (non-circumscribed) otherwise. We also investigated the performance of the features for consensus masses, defined as those labeled as spiculated (circumscribed) or nonspiculated (non-circumscribed) by all three radiologists. When masses were labeled according to radiologists R1, R2, and R3 individually, the spiculation feature had an area A_z under the ROC curve of 0.90±0.04, 0.90±0.03, 0.88±0.03, respectively, while the circumscribed margin feature had an A_z value of 0.77±0.04, 0.74±0.04, and 0.80±0.03, respectively. When masses were labeled according to the majority rule, the A_z values for the spiculation and the circumscribed margin features were 0.92±0.03 and 0.80±±0.03, respectively. When only the consensus masses were considered, the A_z values for the spiculation and the circumscribed margin features were 0.96±0.04 and 0.87±0.04, respectively. We conclude that the newly developed features had high accuracy for characterizing mass margins according to BI-RADS descriptors.

Proceedings Article | 17 March 2008 Paper

Comparison of mammographic parenchymal patterns of normal subjects and breast cancer patients

Yi-Ta Wu, Berkman Sahiner, Heang-Ping Chan, Jun Wei, Lubomir Hadjiiski, Mark Helvie, Yiheng Zhang, Jiazheng Shi, Chuan Zhou, Jun Ge, Jing Cui

Proceedings Volume 6915, 691520 (2008) https://doi.org/10.1117/12.771278

KEYWORDS: Breast, Mammography, Breast cancer, Cancer, Image segmentation, Received signal strength, Feature extraction, Statistical analysis, Computer aided diagnosis and therapy, Computing systems

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Proceedings Article | 17 March 2008 Paper

Breast mass segmentation on dynamic contrast-enhanced magnetic resonance scans using the level set method

Jiazheng Shi, Berkman Sahiner, Heang-Ping Chan, Chintana Paramagul, Lubomir Hadjiiski, Mark Helvie, Yi-Ta Wu, Jun Ge, Yiheng Zhang, Chuan Zhou, Jun Wei

Proceedings Volume 6915, 69152A (2008) https://doi.org/10.1117/12.771390

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, 3D image processing, Magnetism, Breast cancer, 3D acquisition, 3D scanning, 3D modeling, Mathematical modeling

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Proceedings Article | 17 March 2008 Paper

Design and evaluation of a new automated method for the segmentation and characterization of masses on ultrasound images

Jing Cui, Berkman Sahiner, Heang-Ping Chan, Alexis Nees, Chintana Paramagul, Lubomir Hadjiiski, Chuan Zhou, Jiazheng Shi

Proceedings Volume 6915, 69150H (2008) https://doi.org/10.1117/12.770300

KEYWORDS: Image segmentation, Feature extraction, Computer aided diagnosis and therapy, Ultrasonography, Breast, Computer aided design, Image classification, Computing systems, Feature selection, Receivers

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Segmentation of masses is the first step in most computer-aided diagnosis (CAD) systems for characterization of breast masses as malignant or benign. In this study, we designed an automated method for segmentation of masses on ultrasound (US) images. The method automatically estimated an initial contour based on a manually-identified point approximately at the mass center. A two-stage active contour (AC) method iteratively refined the initial contour and performed self-examination and correction on the segmentation result. To evaluate our method, we compared it with manual segmentation by an experienced radiologists (R1) on a data set of 226 US images containing biopsy-proven masses from 121 patients (44 malignant and 77 benign). Four performance measures were used to evaluate the segmentation accuracy; two measures were related to the overlap between the computer and radiologist segmentation, and two were related to the area difference between the two segmentation results. To compare the difference between the segmentation results by the computer and R1 to inter-observer variation, a second radiologist (R2) also manually segmented all masses. The two overlap measures between the segmentation results by the computer and R1 were 0.87+ 0.16 and 0.73+ 0.17 respectively, indicating a high agreement. However, the segmentation results between two radiologists were more consistent. To evaluate the effect of the segmentation method on classification accuracy, three feature spaces were formed by extracting texture, width-to-height, and posterior shadowing features using the computer segmentation, R1's manual segmentation, and R2's manual segmentation. A linear discriminant analysis classifier using stepwise feature selection was tested and trained by a leave-one-case-out method to characterize the masses as malignant or benign. For case-based classification, the area A_z under the test receiver operating characteristic (ROC) curve was 0.90±0.03, 0.87±0.03 and 0.87±0.03 for the feature sets based on computer segmentation, R1's manual segmentation, and R2's manual segmentation, respectively.

Proceedings Article | 17 March 2008 Paper

Automated detection of breast vascular calcification on full-field digital mammograms

Jun Ge, Heang-Ping Chan, Berkman Sahiner, Chuan Zhou, Mark Helvie, Jun Wei, Lubomir Hadjiiski, Yiheng Zhang, Yi-Ta Wu, Jiazheng Shi

Proceedings Volume 6915, 691517 (2008) https://doi.org/10.1117/12.773096

KEYWORDS: Image segmentation, Computer aided diagnosis and therapy, Mammography, Breast, Algorithm development, Blood vessels, Feature selection, Computing systems, Arteries

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Breast vascular calcifications (BVCs) are calcifications that line the blood vessel walls in the breast and appear as parallel or tubular tracks on mammograms. BVC is one of the major causes of the false positive (FP) marks from computer-aided detection (CADe) systems for screening mammography. With the detection of BVCs and the calcified vessels identified, these FP clusters can be excluded. Moreover, recent studies reported the increasing interests in the correlation between mammographically visible BVCs and the risk of coronary artery diseases. In this study, we developed an automated BVC detection method based on microcalcification prescreening and a new k-segments clustering algorithm. The mammogram is first processed with a difference-image filtering technique designed to enhance calcifications. The calcification candidates are selected by an iterative process that combines global thresholding and local thresholding. A new k-segments clustering algorithm is then used to find a set of line segments that may be caused by the presence of calcified vessels. A linear discriminant analysis (LDA) classifier was designed to reduce false segments that are not associated with BVCs. Four features for each segment selected with stepwise feature selection were used for this LDA classification. Finally, the neighboring segments were linked and dilated with morphological dilation to cover the regions of calcified vessels. A data set of 16 FFDM cases with vascular calcifications was collected for this preliminary study. Our preliminary result demonstrated that breast vascular calcifications can be accurately detected and the calcified vessels identified. It was found that the automated method can achieve a detection sensitivity of 65%, 70%, and 75% at 6.1 mm, 8.4 mm, and 12.6mm FP segments/image, respectively, without any true clustered microcalcifications being falsely marked. Further work is underway to improve this method and to incorporate it into our FFDM CADe system.

Proceedings Article | 5 April 2007 Paper

Pulmonary nodule registration in serial CT scans using rib anatomy and nodule template matching

Jiazheng Shi, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Chuan Zhou, Yi-Ta Wu, Jun Wei

Proceedings Volume 6514, 65140R (2007) https://doi.org/10.1117/12.713409

KEYWORDS: Computed tomography, Lung, Image registration, 3D acquisition, Image segmentation, Spine, Expectation maximization algorithms, Bone, Detection and tracking algorithms, Error analysis

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Proceedings Article | 31 March 2007 Paper

The effect of nodule segmentation on the accuracy of computerized lung nodule detection on CT scans: comparison on a data set annotated by multiple radiologists

Berkman Sahiner, Lubomir Hadjiiski, Heang-Ping Chan, Jiazheng Shi, Ted Way, Philip Cascade, Ella Kazerooni, Chuan Zhou, Jun Wei

Proceedings Volume 6514, 65140L (2007) https://doi.org/10.1117/12.713771

KEYWORDS: Roentgenium, Lung, Computed tomography, Image segmentation, Feature extraction, CAD systems, Computing systems, Space operations, 3D modeling, Detection and tracking algorithms

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Proceedings Article | 20 March 2007 Paper

Effect of CAD on radiologists’ detection of lung nodules on thoracic CT scans: observer performance study

Berkman Sahiner, Lubomir Hadjiiski, Heang Chan, Jiazheng Shi, Philip Cascade, Ella Kazerooni, Chuan Zhou, Jun Wei, Aamer Chughtai, Chad Poopat, Thomas Song, Jadranka Nojkova, Luba Frank, Anil Attili

Proceedings Volume 6515, 65151D (2007) https://doi.org/10.1117/12.709851

KEYWORDS: Computer aided diagnosis and therapy, Computing systems, Lung, Computed tomography, Computer aided design, Radiology, Statistical analysis, Gold, Lung imaging, Databases

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Proceedings Article | 8 March 2007 Paper

A dynamic multiple thresholding method for automated breast boundary detection in digitized mammograms

Yi-Ta Wu, Chuan Zhou, Lubomir Hadjiiski, Jiazheng Shi, Jun Wei, Chintana Paramagul, Berkman Sahiner, Heang-Ping Chan

Proceedings Volume 6512, 65122U (2007) https://doi.org/10.1117/12.710198

KEYWORDS: Breast, Mammography, Bragg cells, Image segmentation, Detection and tracking algorithms, Image analysis, Image filtering, Distance measurement, Radiology, Image processing algorithms and systems

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Showing 5 of 12 publications

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