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http://dx.doi.org/10.9718/JBER.2007.28.1.162

A Study on the Multi-View Based Computer Aided Diagnosis in Digital Mammography  

Choi, Hyoung-Sik (Department of Biomedical Engineering, Hanyang University)
Cho, Yong-Ho (Department of Biomedical Engineering, Hanyang University)
Cho, Baek-Hwan (Department of Biomedical Engineering, Hanyang University)
Moon, Woo-Kyoung (Department of Diagnostic Radiology, College of Medicine, Seoul National University)
Im, Jung-Gi (Department of Diagnostic Radiology, College of Medicine, Seoul National University)
Kim, In-Young (Department of Biomedical Engineering, Hanyang University)
Kim, Sun-I. (Department of Biomedical Engineering, Hanyang University)
Publication Information
Journal of Biomedical Engineering Research / v.28, no.1, 2007 , pp. 162-168 More about this Journal
Abstract
For the past decade, the full-field digital mammography has been widely used for early diagnosis of breast cancer, and computer aided diagnosis has been developed to assist physicians as a second opinion. In this study, we try to predict the breast cancer using both mediolateral oblique(MLO) view and craniocaudal(CC) view together. A skilled radiologist selected 35 pairs of ROIs from both MLO view and CC view of digital mammogram. We extracted textural features using Spatial Grey Level Dependence matrix from each mammogram and evaluated the generalization performance of the classifier using Support Vector Machine. We compared the multi-view based classifier to single-view based classifier that is built from each mammogram view. The results represent that the multi-view based computer aided diagnosis in digital mammogram could improve the diagnostic performance and have good possibility for clinical use to assist physicians as a second opinion.
Keywords
digital mammogram; textural features; support vector machine; multi-view analysis;
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