An Optimized Selective Scale Space Based Fuzzy C-Means Model for Image Segmentation

Abstract

Clustering is a widely used technique for segmentation of images. In this paper, a method is proposed for image segmentation, which used an Optimized Selective Scale Based Fuzzy c-Means approach. This approach is used to improve the quality of image segmentation. An algorithm named Fuzzy c-means (FCM) is used for data clustering in which an element can belong to multiple clusters. This algorithm results in the transformation of data elements in such a way that closer elements will come more closer and remaining elements will scatter farther. Genetic Algorithm is used as an optimization technique in this model. Genetic Algorithm is one of the commonly used methods to decide the optimal value of a criterion. The optimal value is determined by simulating the evolution of population until the best fitted individuals among the population is not encountered. It is obtained by mutation selection and crossover of individuals from the existing population.