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

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

doi:10.1117/1.JMI.2.2.024501

3 April 2015 Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

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

Author Affiliations +

Journal of Medical Imaging, Vol. 2, Issue 2, 024501 (April 2015). https://doi.org/10.1117/1.JMI.2.2.024501

Abstract

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.

Citation Download Citation

Brad M. Keller, Andrew Oustimov, Yan Wang, Jinbo Chen, Raymond J. Acciavatti, Yuanjie Zheng, Shonket Ray, James C. Gee, Andrew D. A. Maidment, and 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

Published: 3 April 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available