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Segmentation is used as the first and the most important phase in the recognition and treatment of a disease in analyzing medical images like MRI images. It is challenging due to poor contrast and artifacts that result in missing or diffuse organ/tissue boundaries. In this paper, we will discuss different ways to automate the segmentation of medical images. We except further improvements can be achieved by incorporating as much prior information as possible (e.g. texture, shape, & spatial location of organs) into a single framework.

This paper present a Improved Algorithm for Image Segmentation System for a RGB colour image, and presents a proposed efficient colour image segmentation algorithm based on evolutionary approach i.e. improved Genetic algorithm. The proposed technique, without any predefined parameters determines the optimum number of clusters for colour images. The optimal number of clusters is obtained by using maximum fitness value of population selection. The advantage of this method lies in the fact that no prior knowledge related to number of clusters is required to segment the color image. Proposed algorithm strongly supports the better quality of segmentation. Experiments on standard images have given the satisfactory and comparable results with other techniques.