Texture features analysis technique to detect mass lesion in digitized mammogram images

Mass lesions are one of the breast cancer tumors. Mammogram images first screening tool to detect tumors in women breast, but due radiologist fatigue, number false positive (FP) and negative (FN) rates increased. The main objective this paper is develop an intelligent computer aided diagnosis (CAD) system that can accurately mass digitized mammogram images. proposed method has three stages. stage a preprocessing stage, where lesion enhanced using customized Laplacian filter. Then, multi-statistical filters implemented potential In final detected FP regions reduced five texture features. algorithm evaluated 45 achieved accuracy rate 97% detecting with 83% sensitivity 98% specificity rate.