Mammogram breast density classification using mean-elliptical local binary patterns

Minu George; Erika Denton; Reyer Zwiggelaar

doi:10.1117/12.2318057

6 July 2018 Mammogram breast density classification using mean-elliptical local binary patterns

Minu George, Erika Denton, Reyer Zwiggelaar

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107180B (2018) https://doi.org/10.1117/12.2318057
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

This paper presents a novel variant of Local Binary Patterns (LBP) so called Mean-Elliptical Local Binary Patterns (M-ELBP) for breast density classification. The basic idea of (E)LBP is that an image texture consists of micropatterns and the histogram of these micropatterns could give information about the local features in an image. To include intensity based information along with texture features, we improve the ELBP descriptor by including the mean intensity around the central pixel. We use multiple orientations which facilitates capturing sufficient micropattern features from the mammographic images. The M-ELBP is computed for the fibroglandular disk area instead of the whole breast region. A Bayesian network classifier was used for performing mammogram density classification and a stratified ten-fold cross-validation scheme was used for performance evaluation of the proposed method. The proposed method achieved an accuracy of 74% which is comparable with other methods in the literature using the same dataset.

Citation Download Citation

Minu George, Erika Denton, and Reyer Zwiggelaar "Mammogram breast density classification using mean-elliptical local binary patterns", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107180B (6 July 2018); https://doi.org/10.1117/12.2318057

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