Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

Shelda Sajeev; Mariusz Bajger; Gobert Lee; Chisako Muramatsu; Hiroshi Fujita

doi:10.1117/12.2564272

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22 May 2020 Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

Shelda Sajeev, Mariusz Bajger, Gobert Lee, Chisako Muramatsu, Hiroshi Fujita

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Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 1151323 (2020) https://doi.org/10.1117/12.2564272
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium

Abstract

The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.

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Shelda Sajeev, Mariusz Bajger, Gobert Lee, Chisako Muramatsu, and Hiroshi Fujita "Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 1151323 (22 May 2020); https://doi.org/10.1117/12.2564272

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