Mammographic Image Segmentation using Automatic Evolutionary Algorithms

Abstract

Breast cancer is noticed as the most common cancer and second main cause of cancerous deaths among women population in world wide. Many studies publicized that the early detection of breast cancer can cause longer survival rate. With the advent of new technologies in mammography, abnormality of masses can be effectively detected and diagnosed. Such Computer Aided Diagnosis and Detection(CADx, CADe) systems are automated and semi-automated systems help radiologists in early detection and diagnosis of breast cancer. The common phases of a CAD system are segmentation, feature extraction and classification. Efficient segmentation certainly can influence the subsequent phases of these systems. Clustering is a most common machine learning method in many segmentation applications including mammogram segmentation. Most of the segmentation methods are based on pixel intensity values and usually the segment with maximum intensity values is the region of interest. But in practice the complete segment may not contain abnormality. Abnormal mass is generally hard. This paper proposes a two phase evolutionary based segmentation combined with feature extraction for detection of regions of interests in mammograms using recent Automatic Clustering with simultaneous Feature Subset Selection for gray scale Image segmentation using Differential Evolution (ACFSDE).The proposed method has two phases; in the first phase suspected region is determined using automatic evolutionary intensity based segmentation. From the region hard mass area is determined using spatial information based segmentation. The second phase extracts textural features of each pixel by constructing GLCM which follows ACFSDE. Experiments are conducted on each image of MIAS database. The results demonstrated the accuracy and efficiency of the algorithm in identifying the masses of mammograms and the results are validated with ground truth.