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http://dx.doi.org/10.9717/kmms.2013.16.5.558

Improvement of Sparse Representation based Classifier using Fisher Discrimination Dictionary Learning for Malignant Mass Detection  

Kim, Seong Tae (한국과학기술원 전기 및 전자공학과)
Lee, Seung Hyun (한국과학기술원 전기 및 전자공학과)
Min, Hyun-Seok (한국과학기술원 전기 및 전자공학과)
Ro, Yong Man (한국과학기술원 전기 및 전자공학과)
Publication Information
Journal of Korea Multimedia Society / v.16, no.5, 2013 , pp. 558-565 More about this Journal
Abstract
Mammography, the process of using X-ray to examine the woman breast, is the one of the effective tools for detecting breast cancer at an early state. In screening mammogram, Computer-Aided Detection(CAD) system helps radiologist to diagnose cases by detecting malignant masses. A mass is an important lesion in the breast that can indicate a cancer. Due to various shapes and unclear boundaries of the masses, detecting breast masses is considered a challenging task. To this end, CAD system detects a lot of regions of interest including normal tissues. Thus it is important to develop the well-organized classifier. In this paper, we propose an enhanced sparse representation (SR) based classifier using Fisher discrimination dictionary learning. Experimental results show that the proposed method outperforms the existing support vector machine (SVM) classifier.
Keywords
Computer-Aided detection; Breast masses; Sparse representation; Dictionary learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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