Analysis of Breast Thermograms Based on Statistical Image Features and Hybrid Fuzzy Classification

Authors:
Gerald Schaefer;Tomoharu Nakashima;Michal Zavisek
Affiliations:
School of Engineering and Applied Science, Aston University, U.K;Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Japan;Faculty of Electrical Engineering and Communication, Brno University of Technology, Czech Republic
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

Citing 2
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Abstract

Breast cancer is the most commonly diagnosed form of cancer in women accounting for about 30% of all cases. Medical thermography has been shown to be well suited for the task of detecting breast cancer, in particular when the tumour is in its early stages or in dense tissue. In this paper we perform breast cancer analysis based on thermography. We employ a series of statistical features extracted from the thermograms which describe bilateral differences between left and right breast areas. These features then form the basis of a hybrid fuzzy rule-based classification system for diagnosis. The rule base of the classifier is optimised through the application of a genetic algorithm which ensures a small set of rules coupled with high classification performance. Experimental results on a large dataset of nearly 150 cases confirm the efficacy of our approach.