Detection and classification of calcifications in digital mammograms by multi-scale and multi-position

Authors:
Z. Lifeng;Chen Ying;Zhang Lu
Affiliations:
Department of Biomedical Engineering, School of Medicine, Shanghai Jiao Tong University, Chongqing Rd. 227, Shanghai, 200025, China.;Department of Biomedical Engineering, School of Medicine, Shanghai Jiao Tong University, Chongqing Rd. 227, Shanghai, 200025, China.;Department of Biomedical Engineering, School of Medicine, Shanghai Jiao Tong University, Chongqing Rd. 227, Shanghai, 200025, China
Venue:
International Journal of Computational Science and Engineering
Year:
2012

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Abstract

In this paper, we present a novel multi-scale and multi-position classification method. In this method, mammograms are divided into sub-images from which the image features can be extracted. The proposed method uses a cascaded support vector machine (SVM) classifier to detect and classify calcifications. Using proposed method, we can robotically detect and classify calcifications into different types. This method appropriately meets the basic requirements in CAD-based mammographic screening. The experiments data of the method from digital database for screening mammography (DDSM) show that the detection rate of calcifications can reach up to 98% with 23.75% FP.