Breast thermography is a non-invasive imaging technique used for early detection of breast cancer based on temperatures. Temperature matrix of breast provides minute variations in temperatures, which is significant in early detection of breast cancer. The minimum, maximum temperatures and the their range may be different for each breast thermogram. Normalization of temperature matrices of breast thermograms is essential to bring the different range of temperatures to the common scale. In this article, we demonstrate the importance of temperature matrix normalization of breast thermograms. This paper also proposes a novel method for automatically classifying breast thermogram images using local energy features of wavelet sub-bands. A significant subset of features is selected by a random subset feature selection (RSFS) and genetic algorithm. Features selected by RSFS method are found to be relevant in detection of asymmetry between right and left breast. We have obtained an accuracy of 91%, sensitivity 87.23% and specificity 94.34% using SVM Gaussian classifier for normalized breast thermograms. Accuracy of classification between a set of hundred normalized and corresponding set of non-normalized breast thermograms are compared. An increase in accuracy of 16% is obtained for normalized breast thermograms in comparison with non-normalized breast thermograms.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design