Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution. Issue 2 (November 2015)

• Record Type: Journal Article
• Authors: Pak, Fatemeh; Kanan, Hamidreza Rashidy; Alikhassi, Afsaneh
• Is Part Of: Computer methods and programs in biomedicine. Volume 122:Issue 2(2015)
• Page Start: 89
• Page End: 107
• Publication Date: 2015-11
• Subjects: Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.06.009
• Languages: English
• ISSNs: 0169-2607

Abstract

<abstract abstract-type="author-highlights" id="abs0005"> <title id="sect0005">Highlights</title> <sec> <p id="spar0005"> <list id="lis0005"> <list-item id="lsti0005"> <label></label> <p id="par0005">We propose a new algorithm for breast cancer detection and classification.</p> </list-item> <list-item id="lsti0010"> <label></label> <p id="par0010">Our system can accurately detect the probability of benign or malign breast lesion.</p> </list-item> <list-item id="lsti0015"> <label></label> <p id="par0015">We utilize NSCT transform and super resolution to improve the quality of the images.</p> </list-item> <list-item id="lsti0020"> <label></label> <p id="par0020">The results on MIAS database show significant performance of the proposed method.</p> </list-item> <list-item id="lsti0025"> <label></label> <p id="par0025">Our system achieves 91.43% and 6.42% as a mean accuracy and FPR, respectively.</p> </list-item> </list> </p> </sec> </abstract>