A Novel Approach for Tumor Detection in Mammography Images

Abstract

Breast cancer is one of the major causes of death among women in recent decades. Screening mammography is currently the best available radiological technique for early detection of breast cancer. In recent years, several methods have been used for automated tumor detection in mammography images. In some methods, due to a variety of processing and multiple operations on images, there are many computational complexities and much time overhead. In other methods the recognition accuracy is relatively low. In this paper, a new method to detect cancerous lesions in mammography images is presented using cellular learning automata algorithm. Cellular learning automata algorithm is well suited for image processing, because it is cellular and belongs pixels like an image. Distributed performance and parallel processing properties of this method has optimal results in image processing. Experimental results show the effectiveness of the proposed method.