Medical Image Segmentation via Optimized K-Means

The main purpose of this paper is to identify the suitable algorithm for image segmentation. Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods by various researches for medical image segmentation have been presented in this paper. In the course of the comparative analysis, it is evidently demonstrated that the traditional k-means is not appropriate for segmenting the medical images. But K-means gives the best results for image segmentation with smaller number of K - values. Accordingly K-means can be mixtured with snake optimization method and Normalized Cut algorithm and the proposed algorithm is labeled as optimized K-means.