Abstract
This paper presents a SVM based approach to microcalcification clusters (MCs) detection in mammograms by embedding general tensor discriminant Analysis (GTDA) subspace learning. In the approach GTDA and other subspace learning methods are employed to extract subspace features. In extracted feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and SVM is used as a classifier to make decision for the presence of MCs or not. A large number of experiments are carried out to evaluate and compare the performance of the proposed MCs detection algorithms. The experiment result suggests that the proposed method is a promising technique for MCs detection.
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Zhang, X., Wang, M., Yu, F. (2012). A SVM Approach for MCs Detection by Embedding GTDA Subspace Learning. In: Jin, D., Lin, S. (eds) Advances in Electronic Engineering, Communication and Management Vol.2. Lecture Notes in Electrical Engineering, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27296-7_15
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DOI: https://doi.org/10.1007/978-3-642-27296-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27295-0
Online ISBN: 978-3-642-27296-7
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