[01] Nader Abd El-Rahman Mohamed, Biomedical Engineering Department, Misr University for Science and Technology (MUST), Cairo, Egypt.

Early breast cancer detection, is one of the most important areas that researchers are working on, and it can increase the rate of diagnosis, cure and survival of the affected women. Considering the high cost of treatment as well as the high prevalence of the disease among women, early diagnosis will be the most significant step in reducing the health and social complications of this disease. Breast cancer is the major cause of cancer-related mortality among women worldwide. Early detection of cancer, especially breast cancer, will facilitate the treatment process. Cancer isranked as the 3rd cause of death. The aim was to develop an automatic breast cancer detection software that uses image processing techniques to analyze thermal breast images to detect the signs shown in these images for early detection of breast cancer. MATLAB was used as the programming environment. A new algorithm is proposed for the extraction of the breast characteristic features based on image analysis and image statistics. These features are extracted from the thermal image captured by a thermal camera. These features can be used to classify the breast either to be normal or suspected cancerous using a Neural Network classifier. The algorithm was tested with 206 breast images. Success rate of 96.12% is reached.

Cancer Detection, Breast Thermal Image, Thermography, Image Analysis, Image Statistics

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