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J. Imaging 2019, 5(2), 24; https://doi.org/10.3390/jimaging5020024

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
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This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Southampton, UK, 9–11 July 2018.
Received: 30 November 2018 / Revised: 25 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Medical Image Understanding and Analysis 2018)
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Abstract

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region. View Full-Text
Keywords: breast density classification; risk estimation; local binary patterns; texture descriptors breast density classification; risk estimation; local binary patterns; texture descriptors
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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George, M.; Zwiggelaar, R. Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring. J. Imaging 2019, 5, 24.

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