An intelligent hybrid method for the diagnosis, segmentation and classification of breast tumors based on new tissue features extracted from two views of mammography images

Breast cancer is one of the most important cancers among women. Usually, screening for breast cancer is mammography, which reduced the death rate caused by it. The purpose of this paper is to introduce a new hybrid intelligent method for classification of breast tissue into two healthy and unhealthy types by simultaneous examination of two aspects of mammogram images and segmentation of unplanned unhealthy tissue. To this purpose, a new hybrid method including clustering and region growth algorithms are used to identification of the suspected area to the tumor presence. The suspected area is identifiedby combining the FCM clustering and the region growth algorithms after removed the background, and was segmented tumor using the morphological processes. Then, was done classification of the breast tissue into two types of healthy and unhealthyusingsimultaneous two standard views of mammogram (MLO and CC) of a breast, and the extraction of tissue features based on the gray-level co-occurrence matrix,and c and the brightest of intensity level of the cluster center features.Also, was introduced and used the brightest of intensity level of the cluster center features for the first time. Finally, the extracted features are considered as inputs of a fuzzy system for classification of breast tissue. The results of this study are shown the proposed method has accuracy 97.7% inthe breast tissue classification on 300 pairs of mammograms. Also, it is shown that the simultaneous examination of features of the two views standard mammograms can be useful in early detection of breast cancer.