Shape and Texture Features for the Identification of Breast Cancer

this paper aims to develop intelligent breast cancer identification system based image processing techniques and neural network classifier. Recently, many researchers have developed image classification systems for classifying breast tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classification of breast cancer images in this proposed system has been performed based on the shape and texture characteristics of the images. Thus, we extract two kinds of features: shape and texture. The asymmetry, roundness, intensity levels and more are the real shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for the extraction of tumors: Thresholding, filtering, adjustments, canny edge detection, and some morphological operations. Texture features are then extracted using GLCM algorithm, while the shape features are extracted directly from the images. The experimental results show a great identification rate of 92%.