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11 March 2005 Detection of mass tumors in mammograms using SVD subspace analysis
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Proceedings Volume 5674, Computational Imaging III; (2005)
Event: Electronic Imaging 2005, 2005, San Jose, California, United States
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eugene T. Lin, Yuxin Liu, and Edward J. Delp III "Detection of mass tumors in mammograms using SVD subspace analysis", Proc. SPIE 5674, Computational Imaging III, (11 March 2005);

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