Dr. Yi-Ta Wu Profile

Dr. Yi-Ta Wu

at Michigan Medicine

SPIE Involvement:

Author

Publications (13)

Proceedings Article | 19 March 2008 Paper

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

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

Paper

Classification of breast masses and normal tissues in digital tomosynthesis mammography

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

Proceedings Volume 6915, 691508 (2008) https://doi.org/10.1117/12.771189

KEYWORDS: Breast, Digital mammography, Tissues, Statistical analysis, Computer aided diagnosis and therapy, Reconstruction algorithms, Feature extraction, Breast cancer, Photovoltaics, Image segmentation

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Proceedings Article | 17 March 2008 Paper

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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Proceedings Article | 17 March 2008 Paper

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

Paper

Digital tomosynthesis mammography: comparison of mass classification using 3D slices and 2D projection views

Heang-Ping Chan, Yi-Ta Wu, Berkman Sahiner, Yiheng Zhang, Jun Wei, Richard Moore, Daniel Kopans, Mark Helvie, Lubomir Hadjiiski, Ted Way

Proceedings Volume 6915, 691506 (2008) https://doi.org/10.1117/12.770422

KEYWORDS: Digital breast tomosynthesis, Photovoltaics, Feature extraction, Digital mammography, Image segmentation, Computer aided diagnosis and therapy, Breast, Mammography, Reconstruction algorithms, Feature selection

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Proceedings Article | 17 March 2008 Paper

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

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

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 | 29 March 2007 Paper

Paper

Computer-aided detection of breast masses on prior mammograms

Jun Wei, Berkman Sahiner, Heang-Ping Chan, Lubomir Hadjiiski, Marilyn Roubidoux, Mark Helvie, Jun Ge, Chuan Zhou, Yi-Ta Wu

Proceedings Volume 6514, 651405 (2007) https://doi.org/10.1117/12.713768

KEYWORDS: Mammography, CAD systems, Computer aided design, Statistical analysis, Cancer, Computer aided diagnosis and therapy, Visibility, Breast, Feature extraction, Feature selection

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Proceedings Article | 8 March 2007 Paper

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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Proceedings Article | 17 March 2006 Paper

Paper

Regularized discriminate analysis for breast mass detection on full field digital mammograms

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

Proceedings Volume 6144, 61445P (2006) https://doi.org/10.1117/12.654897

KEYWORDS: Feature selection, Feature extraction, Mammography, CAD systems, Breast, Computer aided design, Tissues, Computer aided diagnosis and therapy, Classification systems

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In computer-aided detection (CAD) applications, an important step is to design a classifier for the differentiation of the abnormal from the normal structures. We have previously developed a stepwise linear discriminant analysis (LDA) method with simplex optimization for this purpose. In this study, our goal was to investigate the performance of a regularized discriminant analysis (RDA) classifier in combination with a feature selection method for classification of the masses and normal tissues detected on full field digital mammograms (FFDM). The feature selection scheme combined a forward stepwise feature selection process and a backward stepwise feature elimination process to obtain the best feature subset. An RDA classifier and an LDA classifier in combination with this new feature selection method were compared to an LDA classifier with stepwise feature selection. A data set of 130 patients containing 260 mammograms with 130 biopsy-proven masses was used. All cases had two mammographic views. The true locations of the masses were identified by experienced radiologists. To evaluate the performance of the classifiers, we randomly divided the data set into two independent sets of approximately equal size for training and testing. The training and testing were performed using the 2-fold cross validation method. The detection performance of the CAD system was assessed by free response receiver operating characteristic (FROC) analysis. The average test FROC curve was obtained by averaging the FP rates at the same sensitivity along the two corresponding test FROC curves from the 2-fold cross validation. At the case-based sensitivities of 90%, 80% and 70% on the test set, our RDA classifier with the new feature selection scheme achieved an FP rate of 1.8, 1.1, and 0.6 FPs/image, respectively, compared to 2.1, 1.4, and 0.8 FPs/image with stepwise LDA with simplex optimization. Our results indicate that RDA in combination with the sequential forward inclusion-backward elimination feature selection method can improve the performance of mass detection on mammograms. Further work is underway to optimize the feature selection and classification scheme and to evaluate if this approach can be generalized to other CAD classification tasks.

Proceedings Article | 10 March 2006 Paper

Paper

Computer-aided detection of breast masses on mammograms: bilateral analysis for false positive reduction

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

Proceedings Volume 6144, 614421 (2006) https://doi.org/10.1117/12.655262

KEYWORDS: Mammography, CAD systems, Breast, Tissues, Feature extraction, Computing systems, Breast cancer, Computer aided design, Computer aided diagnosis and therapy

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Showing 5 of 13 publications

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