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A full automatic technique and a user friendly toolbox developed to assist physicians in early clinical detection of breast cancer. Database contains different degrees of thermal images obtained from normal or cancerous mammary tissues of patients with mean age of 42.3 years (SD: ±10.50) which their sympathetic nervous system activated with a cold stimulus on hands. First ROI was determined using full automatic operation and the quality of image improved. Then, some features, including statistical, morphological, frequency domain, histogram and GLCM features were extracted from segmented right and left breast. Subsequently, to achieve the best feature space decreasing complexity and increasing accuracy, feature selectors such as mRMR, SFS, SBS, SFFS, SFBS and GA have been used. Finally to classify and TH labeling, supervised learning techniques such as AdaBoost, SVM, kNN, NB and PNN were applied and compared with each other to find the best suitable one. The experimental results obtained on native database showed the mean accuracy of 88.03% for 0 degree images using combination of mRMR and AdaBoost and for combined 3 degrees using combination of GA and AdaBoost. The maximum accuracy obtained from all degrees and their combinations in before and after ice test is nearly to 100%.

scientia iranica

Developing a Toolbox for Clinical Preliminary Breast Cancer Detection in Different views of Thermogram Images using a Set of Optimal Supervised Classifiers