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Breast cancer segmentation based on modified Gaussian mean shift algorithm for infrared thermal images

ABSTRACT Accurate segmentation of infrared thermal images is a challenging issue in the breast cancer detection. This paper presents and evaluates novel segmentation method for breast cancer detection using infrared thermal images. The developed segmentation method uses developed segmentation algorithm that is hybrid of Gaussian Mean Shift (GMS) and roulette wheel selection approach. In the first stage of the developed segmentation method, the redundant portions of the infrared thermal image are removed and then, the infrared thermal image is divided into five and six clusters by applying the developed segmentation algorithm. The segmented infrared thermal image is created by multiplying the selected best image of each cluster. This image is used to diagnosis the normal breast from abnormal breast. The proposed method is developed using MATLAB 2017 and infrared thermal images of 64 patients. The results show that the average Dice similarity coefficient, Jaccard index and Hausdorff distance in the proposed segmentation method are 91.81%, 84.86%, and 4.87, respectively. The comparison results demonstrate that using the proposed segmentation method can improve the performance of the Computer-Aided-Detection (CAD) system compared to the CAD systems that use the Mean Shift (MS) and Fuzzy C-Means (FCM) segmentation algorithms.