Segmentation of thermal infrared breast images using K-means, FCM and EM algorithms for breast cancer detection

Breast cancer is the most commonly occurring cancer among women leading to deaths. Detection of tumors at early stages improves the survival rate. Infrared thermograph images of breast provide information based on temperature changes in the breast. This information is more supportive in detecting cancer at its early stage. In this paper, three segmentation techniques, K-Means, Fuzzy C-Means (FCM) and Gaussian Mixture Model - Expectation Maximization (GMM-EM) are employed to segment the IR breast images and compared. The method is applied to classify the malignant and benign cancer tissues.