Segmentation of mammograms using a novel intuitionistic possibilistic fuzzy c-mean clustering algorithm

Abstract

There is a partitioning of a data set X into c-clusters in clustering analysis. In 1984, fuzzy c-mean clustering was proposed. Later, fuzzy c-mean was used for the segmentation of medical images. Many researchers work to improve the fuzzy c-mean models. In our paper, we proposed a novel intuitionistic possibilistic fuzzy c-mean algorithm. Possibilistic fuzzy c-mean and intuitionistic fuzzy c-mean are hybridized to overcome the problems of fuzzy c-mean. This proposed clustering approach holds the positive points of possibilistic fuzzy c-mean that will overcome the coincident cluster problem, reduces the noise and brings less sensitivity to an outlier. Another approach of intuitionistic fuzzy c-mean improves the basics of fuzzy c-mean by using intuitionistic fuzzy sets. Our proposed intuitionistic possibilistic fuzzy c-mean technique has been applied to the clustering of the mammogram images for breast cancer detector of abnormal images. The experiments result in high accuracy with clustering and breast cancer detection.