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Proceedings Paper

Detection of mass tumors in mammograms using SVD subspace analysis
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Paper Abstract

In this paper, we propose a new region-based method for detecting mass tumors in digital mammograms. Our method uses principal component analysis (PCA) techniques to reduce the image data into a subspace with significantly reduced dimensionality using an optimal linear transformation. After the transformation, classification in the subspace is performed using a nearest neighbor classifier. We consider the detection of only mass abnormalities in this study. Micro calcifications, spiculated lesions, and other abnormalities are not considered. We implemented our method and achieved a 93% correct detection rate for mass abnormalities in our tests.

Paper Details

Date Published: 11 March 2005
PDF: 12 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.596943
Show Author Affiliations
Eugene T. Lin, Purdue Univ. (United States)
Yuxin Liu, Nokia Inc. (United States)
Edward J. Delp, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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