Classification of breast abnormalities in digital mammography using phase-based features

Julia Diaz-Escobar; Vitaly Kober

doi:10.1117/12.2528524

6 September 2019 Classification of breast abnormalities in digital mammography using phase-based features

Julia Diaz-Escobar, Vitaly Kober

Proceedings Volume 11137, Applications of Digital Image Processing XLII; 1113724 (2019) https://doi.org/10.1117/12.2528524
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

Breast cancer is one of the principal causes of death for women in the world. Invasive breast cancer develops in about one in eight women in the United States during her lifetime. Digital mammography is a common technique for early detection of the breast cancer. However, only 84% of breast cancers are detected by interpreting radiologists. Computer Aided Detection (CAD) is a technology designed to help radiologists and to decrease observational errors. Actually, for every true-positive cancer detected by the CAD there are more false predictions, which have to be ignored by radiologists. In this work, a CAD method for detection and classification of breast abnormalities is proposed. The proposed method is based on the local energy and phase congruency approach and a supervised machine learning classifier. Experimental results are presented using digital mammography dataset and evaluated under different performance metrics.

Citation Download Citation

Julia Diaz-Escobar and Vitaly Kober "Classification of breast abnormalities in digital mammography using phase-based features", Proc. SPIE 11137, Applications of Digital Image Processing XLII, 1113724 (6 September 2019); https://doi.org/10.1117/12.2528524

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