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

Paper Details

Date Published: 6 September 2019
PDF: 9 pages
Proc. SPIE 11137, Applications of Digital Image Processing XLII, 1113724 (6 September 2019); doi: 10.1117/12.2528524
Show Author Affiliations
Julia Diaz-Escobar, CICESE (Mexico)
Vitaly Kober, CICESE (Mexico)
Chelyabinsk State Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11137:
Applications of Digital Image Processing XLII
Andrew G. Tescher; Touradj Ebrahimi, Editor(s)

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