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Proceedings Paper

Texture feature standardization in digital mammography for improving generalizability across devices
Author(s): Yan Wang; Brad M. Keller; Yuanjie Zheng; Raymond J. Acciavatti; James C. Gee; Andrew D. A. Maidment; Despina Kontos
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Paper Abstract

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.

Paper Details

Date Published: 28 February 2013
PDF: 7 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867026 (28 February 2013); doi: 10.1117/12.2008149
Show Author Affiliations
Yan Wang, Perelman School of Medicine at the Univ. of Pennsylvania (United States)
Brad M. Keller, Perelman School of Medicine at the Univ. of Pennsylvania (United States)
Yuanjie Zheng, Perelman School of Medicine at the Univ. of Pennsylvania (United States)
Raymond J. Acciavatti, Perelman School of Medicine at the Univ. of Pennsylvania (United States)
James C. Gee, Perelman School of Medicine at the Univ. of Pennsylvania (United States)
Andrew D. A. Maidment, Perelman School of Medicine at the Univ. of Pennsylvania (United States)
Despina Kontos, Perelman School of Medicine at the Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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