Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction

Abstract

Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.