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Feature selection and analysis on mammogram classification

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2 Author(s)
Aijuan Dong ; Dept. of Comput. Sci., Hood Coll., Frederick, MD, USA ; Baoying Wang

Automatic mammogram analysis is important in early breast cancer detection. In this paper, we present a multi-resolution approach to automated classification of mammograms using Gabor filters. Specifically, Gabor filters of different frequencies and orientations have been used to extract textual patterns of mammograms. To increase classification efficiency and reduce feature space, statistic t-test and its p-values for feature selection and weighting are proposed. Experimental results show that Gabor filters are able to extract textual patterns of mammograms, statistical-based feature selection and weighting can be used to further reduce the feature space without degrading the classification performance.

Published in:

Communications, Computers and Signal Processing, 2009. PacRim 2009. IEEE Pacific Rim Conference on

Date of Conference:

23-26 Aug. 2009

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