Automatic Segmentation and Identification of Abnormal Breast Region in Mammogram Images Based on Statistical Features - Mahmood, Faleh; Hussein, Alaa (Iraqi Journal of Science, 2023)
Breast cancer is one of the most common malignant diseases among women;Mammography is at present one of the available method for early detection ofabnormalities which is related to breast cancer. There are different lesions that arebreast cancer characteristic such as masses and calcifications which can be detectedtrough this technique. This paper proposes a computer aided diagnostic system forthe extraction of features like masses and calcifications lesions in mammograms forearly detection of breast cancer. The proposed technique is based on a two-stepprocedure: (a) unsupervised segmentation method includes two stages performedusing the minimum distance (MD) criterion, (b) feature extraction based on Graylevel Co-occurrence matrices GLCM for the identification of masses andcalcifications lesions. The method suggested for the detection of abnormal lesionsfrom mammogram image segmentation and analysis was tested over several imagestaken from National Center for Early Detection of cancer in Baghdad.