Ms. Yan Wang Profile

Yan Wang

at University of Pennsylvania

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Author

Publications (4)

SPIE Journal Paper | 3 April 2015

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

Brad Keller, Andrew Oustimov, Yan Wang, Jinbo Chen, Raymond Acciavatti, Yuanjie Zheng, Shonket Ray, James Gee, Andrew D. Maidment, Despina Kontos

JMI, Vol. 2, Issue 02, 024501, (April 2015) https://doi.org/10.1117/12.10.1117/1.JMI.2.2.024501

KEYWORDS: Lawrencium, Digital mammography, Breast, Imaging systems, Breast cancer, Sensors, Computing systems, Mammography, Tissues, Image segmentation

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Proceedings Article | 19 March 2013 Paper

Testing realism of software breast phantoms: texture analysis of synthetic mammograms

Predrag Bakic, Brad Keller, Yuanjie Zheng, Yan Wang, James Gee, Despina Kontos, Andrew Maidment

Proceedings Volume 8668, 866824 (2013) https://doi.org/10.1117/12.2008173

KEYWORDS: Breast, Mammography, Tissues, Software development, Breast imaging, Image analysis, Imaging systems, X-rays, Computer simulations, Ions

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Proceedings Article | 28 February 2013 Paper

A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

Yuanjie Zheng, Yan Wang, Brad Keller, Emily Conant, James Gee, Despina Kontos

Proceedings Volume 8670, 86701B (2013) https://doi.org/10.1117/12.2008155

KEYWORDS: Breast, Breast cancer, Tissues, Digital mammography, Mammography, Image classification, Cancer, Image segmentation, Software development, Machine vision

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Proceedings Article | 28 February 2013 Paper

Texture feature standardization in digital mammography for improving generalizability across devices

Yan Wang, Brad Keller, Yuanjie Zheng, Raymond Acciavatti, James Gee, Andrew D. A. Maidment, Despina Kontos

Proceedings Volume 8670, 867026 (2013) https://doi.org/10.1117/12.2008149

KEYWORDS: Breast, Imaging systems, Breast cancer, Sensors, Digital mammography, Feature extraction, Image processing, Modulation transfer functions, Control systems, Spatial resolution

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Growing evidence suggests a relationship between mammographic texture and breast cancer risk. For studies performing texture analysis on digital mammography (DM) images from various DM systems, it is important to evaluate if different systems could introduce inherent differences in the images analyzed and how to construct a methodological framework to identify and standardize such effects, if these differences exist. In this study, we compared two DM systems, the GE Senographe 2000D and DS using a validated physical breast phantom (Rachel, Gammex). The GE 2000D and DS systems use the same detector, but a different automated exposure control (AEC) system, resulting in differences in dose performance. On each system, images of the phantom are acquired five times in the Cranio-Caudal (CC) view with the same clinically optimized phototimer setting. Three classes of texture features, namely grey-level histogram, cooccurrence, and run-length texture features (a total of 26 features), are generated within the breast region from the raw DM images and compared between the two imaging systems. To alleviate system effects, a range of standardization steps are applied to the feature extraction process: z-score normalization is performed as the initial step to standardize image intensities, and the parameters in generating co-occurrence features are varied to decrease system differences introduced by detector blurring effects. To identify texture features robust to detectors (i.e. the ones minimally affected only by electronic noise), the distribution of each texture feature is compared between the two systems using the Kolmogorov-Smirnov (K-S) test at 0.05 significance, where features with p>0.05 are deemed robust to inherent system differences. Our approach could provide a basis for texture feature standardization across different DM imaging systems and provide a systematic methodology for selecting generalizable texture descriptors in breast cancer risk assessment.

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