Title :
Detection of Clustered Pleomorphic Micro-Calcifications in Digital Mammograms
Author :
Lifeng, Zhang ; Ying, Chen ; Fang, Zhang ; Lu, Zhang
Author_Institution :
Med. Sch., Shanghai Jiaotong Univ., Shanghai, China
Abstract :
In this paper, we present a novel multi-scale and multi-position classification (MSPC) method for detection of clustered pleomorphic micro-calcifications in digital mammograms. With this method, mammograms are divided into sub-images from which the image features are extracted and a cascaded Support Vector Machine (SVM) classifier is used to detect pleomorphic calcifications. Using the MSPC method, we robotically classify sub-images within a region of interest similar to other ROI methods used in CAD-based mammographic screening. Our experiments with this method using the Digital Database for Screening Mammography (DDSM) data show that the detection rate of clustered pleomorphic calcification (CPMC) can reach up to 97.26% with a 36.84% false positive rate.
Keywords :
CAD; feature extraction; image classification; mammography; medical image processing; support vector machines; CAD-based mammographic screening; cascaded support vector machine classifier; clustered pleomorphic calcification; clustered pleomorphic microcalcification detection; digital database for screening mammography data; digital mammogram; image feature extraction; multiposition classification method; multiscale classification method; subimage classification; Cancer; Design automation; Feature extraction; Noise; Support vector machines; Wavelet transforms; cascaded SVM; clustered pleomorphic calcifications; multi-scale and multi-position;
Conference_Titel :
Biomedical Engineering and Biotechnology (iCBEB), 2012 International Conference on
Conference_Location :
Macau, Macao
Print_ISBN :
978-1-4577-1987-5
DOI :
10.1109/iCBEB.2012.130
