Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image . As image pixels are generally unlabelled, the commonly used approach for the same is clustering . This paper reviews various existing clustering based image segmentation methods . Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods . As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class . Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods . The survey of various performance parameters for the quantitative evaluation of segmentation results is also included . Further, the publicly available benchmark datasets for image-segmentation are briefed.