Copyright © 2007 The Institute of Electronics, Information and Communication Engineers
Special Section on Advanced Image Technology -- Papers |
Normal Mammogram Detection Based on Local Probability Difference Transforms and Support Vector Machines*
1 The authors are with the Department of Electronics and Telecommunication Engineering, King Mongkut's University of Technology Thonburi, Pracha-uthit Road, Bangkok 10140, Thailand. E-mail: s4510108{at}st.kmutt.ac.th, 2 The authors are with the School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, IN 47907–2035, USA., 3 The author is with the Department of Basic Medical Sciences, School of Veterinary Medicine, Purdue University, West Lafayette, IN 47907–1285, USA.
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Abstract |
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Automatic detection of normal mammograms, as a "first look" for breast cancer, is a new approach to computer-aided diagnosis. This approach may be limited, however, by two main causes. The first problem is the presence of poorly separable "crossed-distributions" in which the correct classification depends upon the value of each feature. The second problem is overlap of the feature distributions that are extracted from digitized mammograms of normal and abnormal patients. Here we introduce a new Support Vector Machine (SVM) based method utilizing with the proposed uncrossing mapping and Local Probability Difference (LPD). Crossed-distribution feature pairs are identified and mapped into a new features that can be separated by a zero-hyperplane of the new axis. The probability density functions of the features of normal and abnormal mammograms are then sampled and the local probability difference functions are estimated to enhance the features. From 1,000 ground-truth-known mammograms, 250 normal and 250 abnormal cases, including spiculated lesions, circumscribed masses or microcalcifications, are used for training a support vector machine. The classification results tested with another 250 normal and 250 abnormal sets show improved testing performances with 90% sensitivity and 89% specificity.
Key Words: breast cancer, mammogram, computer-aided diagnosis (CAD), second opinion, automated radiographic reading
Manuscript received April 10, 2006. Manuscript revised July 17, 2006.
* This work was supported in part by a grant from the Ministry University Affair, Thailand.