Image segmentation is considered to be the most important practical aspect of image processing. It is bethought to have its application in medical imaging and also it acts as a clinical diagnostic tool. Medical image segmentation (MIS) is facilitated by automating the depiction of anatomical structures and other region of interest. In case of Computer Aided Diagnosis, MIS is considered to be an initial and essential step. Accuracy of image segmentation algorithms are focused more behind the success of any medical image analysis. Whatever may be the application either in radiotherapy planning or clinical diagnosis and treatment, exact segmentation of medical images are cared. Hence, it remains challenging, unsolved, and sometimes seems to be a complex task too. Various MIS algorithms have emerged, which are not suitable for all images. In this paper, different MIS approaches are categorized with their sub methods and sub fields. Recent techniques for every category are also discussed and the comparison of these approaches with pros and cons is summarized. Using these techniques to develop a new hybrid algorithm will be of very much use in medical diagnosis.