Breast Tissue Classification in Mammograms Using ICA Mixture Models

Authors:
Ioanna Christoyianni;Athanasios Koutras;Evangelos Dermatas;George K. Kokkinakis
Affiliations:
-;-;-;-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 2
Cited 0

Independent component analysis: theory and applications

Independent component analysis: theory and applications
ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

In this paper we present a novel method for recognizing all kinds of abnormalities in digital mammograms using Independent Component Analysis mixture models and two sets of statistical features based on texture analysis. Our approach is concentrated on finding the ICA mixture model parameters that describe in an exclusive and effective way the abnormal and the normal tissue, and with the aid of a supervised probabilistic classifier we are able to successfully recognize suspicious regions in mammograms. Extensive experiments using the MIAS database have shown great accuracy of 98.33% in classifying an unknown regions of suspicion as abnormal and 62.71% as healthy tissue.