Dr. Jing Cui Profile

Dr. Jing Cui

at Michigan Medicine

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Publications (5)

Proceedings Article | 10 March 2009 Paper

Inter- and intra-observer variability in radiologists' assessment of mass similarity on mammograms

Berkman Sahiner, Lubomir Hadjiiski, Heang-Ping Chan, Jing Cui, Chintana Paramagul, Alexis Nees, Mark Helvie

Proceedings Volume 7263, 726315 (2009) https://doi.org/10.1117/12.813425

KEYWORDS: Mammography, Image retrieval, Breast, Biopsy, Computer aided diagnosis and therapy, Radiology, Feature extraction, Human-machine interfaces, Medical imaging, Decision support systems

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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 | 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

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

Characterization of posterior acoustic features of breast masses on ultrasound images using artificial neural network

Jing Cui, Berkman Sahiner, Heang-Ping Chan, Chintana Paramagul, Alexis Nees, Lubomir Hadjiiski, Yi-Ta Wu

Proceedings Volume 6915, 691521 (2008) https://doi.org/10.1117/12.771401

KEYWORDS: Acoustics, Breast, Computing systems, Image segmentation, Ultrasonography, Artificial neural networks, Feature extraction, Computer aided diagnosis and therapy, Image classification, Computer aided design

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