3 April 2015 Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices
Author Affiliations +
J. of Medical Imaging, 2(2), 024501 (2015). doi:10.1117/1.JMI.2.2.024501
An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges–Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., <63  pixels2) and with a larger offset length (i.e., <7  pixels), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Brad M. Keller, Andrew Oustimov, Yan Wang, Jinbo Chen, Raymond J. Acciavatti, Yuanjie Zheng, Shonket Ray, James C. Gee, Andrew D. A. Maidment, Despina Kontos, "Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices," Journal of Medical Imaging 2(2), 024501 (3 April 2015). https://doi.org/10.1117/1.JMI.2.2.024501


Digital mammography


Imaging systems

Breast cancer

Computing systems


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