Breast cancer segmentation using K-means clustering and optimized region-growing technique

Abstract

Breast cancer is one of the major causes of death among women, and early detection may decrease the aggressiveness of the disease. The goal of this paper is to create an automated system that can classify digital mammogram images into benign and malignant. This paper presents a new detection technique of micro-calcifications in mammogram images. An automated technique for identifying breast microcalcifications (MCs) proposed utilizing two-level segmentation processes, first crop the breast area from the image using k-means clustering, then, an optimized region growing (ORG) approach has been used, where multi-seed points and thresholds are generated optimally depending on the color values of the image pixels. Then the texture features are extracted based on Haralick definitions of texture analysis. In addition, three features (cross-correlation coefficient, pearson correlation, and average area of segmented spots) are obtained from the segmented image. Support vector machine (SVM) classifier evaluate the efficiency of the system utilizing the images from the digital database for screening mammography (DDSM) dataset. The results were obtained by utilizing 355 images for training and 85 images for testing. The proposed system's sensitivity reached up to 97.05%, the specificity obtained is 98.52%, and accuracy is 98.2%.