Detection and classification of calcifications in digital mammograms by multi-scale and multi-position
by Z. Lifeng; Chen Ying; Zhang Lu
International Journal of Computational Science and Engineering (IJCSE), Vol. 7, No. 3, 2012

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.

Online publication date: Mon, 22-Sep-2014

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