Automatic Segmentation and Identification of Abnormal Breast Region in Mammogram Images Based on Statistical Features


Breast cancer is one of the most common malignant diseases among women; Mammography is at present one of the available method for early detection of abnormalities which is related to breast cancer. There are different lesions that are breast cancer characteristic such as masses and calcifications which can be detected trough this technique. This paper proposes a computer aided diagnostic system for the extraction of features like masses and calcifications lesions in mammograms for early detection of breast cancer. The proposed technique is based on a two-step procedure: (a) unsupervised segmentation method includes two stages performed using the minimum distance (MD) criterion, (b) feature extraction based on Gray level Co-occurrence matrices GLCM for the identification of masses and calcifications lesions. The method suggested for the detection of abnormal lesions from mammogram image segmentation and analysis was tested over several images taken from National Center for Early Detection of cancer in Baghdad.