Automated RoI Extraction and Pattern Classification of Breast Thermograms

High mortality rate among women in India is mainly due to breast cancer. As mammography is a standard imaging tool used in screening the breast, it sometimes tends to miss certain abnormalities that lie beneath the breast regions. Thermography being a temperature-based functional imaging modality has enough potential to identify the abnormal changes in the breast through asymmetries at an early stage. This paper considers breast thermograms of patients that are normal and abnormal wherein pre-processing is applied to extract the ROI. The pre-processing includes breast region segmentation using horizontal and vertical projection profile approach along with the Otsu method. The features are then extracted from this ROI for which they are also fed as input to the non-linear classifiers for classification of normal and abnormal. The outcome of the paper results in a good accuracy rate that can be efficiently utilized as a Computer-Aided Detection tool for a second opinion in the breast cancer diagnosis.