A novel intuitionistic fuzzy soft set entrenched mammogram segmentation under Multigranulation approximation for breast cancer detection in early stages

Abstract

Automated mammogram image segmentation is one of the most important methods in the domain of medical diagnosis and decision systems. Accurate segmentation of mammogram plays a key role for the detection of any kind of abnormality like lesion tissues, cyst in mammogram images for medical diagnosis. In this study, a novel hybrid soft computing entrenched segmentation method for mammograms is introduced for the detection of breast cancer in early stages. Here, we have designed a novel automatic mammogram segmentation method using intuitionistic fuzzy soft sets (IFSS) and Multigranulation rough set. First, the proposed clustering algorithm accomplishes a soft information structure from the source image using IFSSs via multiple fuzzy membership functions with Yager generating function. The IFSS handles the ambiguity among lesion and non-lesion pixels through the hesitant degree while shaping the membership function. To reduce distant pixels which do not belong to the region of interest (ROI), the lesion tissues in mammogram image is segregated by decision making scheme via a rough approximation of a fuzzy concept under the field of multigranulation space. Later, the proposed scheme utilizes soft-information builder with accuracy and roughness scores on multigranulation approximation space for segregation of normal and abnormal (lesion tissues) pixel from mammograms. The proposed model has generated a threshold image from accuracy and roughness scores via defuzzification process. The proposed segmentation model performs better than the existing methods using evaluation metrics like segmentation accuracy, Jeccards similarity coefficient.