FCM-DCS: Fuzzy C means distorted contour-based segmentation model for breast cancer detection

Abstract

Breast cancer (BC) is a commonly diagnosed cancer among women nowadays. The cancer cells in the breast tissues are known as BC. Comprehensive research on early-stage BC detection helped to increase the survival rate and reduce the amortality rate associated with this disease. Mammogram scan analysis is a commonly used breast tissue visualization method. This image data is analyzed adequately for accurate BC diagnosis. Region of interest (ROI) identification is crucial in an image-based BC detection system. The ROI detection helps to segment the cancer tissues from the mammogram images by analyzing the heterogeneity among cancerous and normal breast tissues. Early-stage BC issues have homogeneous features as normal breast tissues. So, it is an open challenge for the researchers to develop a more accurate segmentation method during the automatic BC stages detection system. This study introduced fuzzy C means (FCM) distorted contour-based segmentation (FCM DCS) method to address the real detection issues in present studies. It uses the distorted contour (DC) based method to identify the contour of the cancer tissue from mammogram images. The DC method is performed with the help of FCM to identify the cancer tissues. Moreover, a histogram and adaptive equalization method were utilized to reduce image noise and preserve the edge features. The result analysis shows that the FCM-DC methods achieved a maximum accuracy rate (98.76 %) than comparison methods in BC detection.