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Image segmentation has been an area of interest for many researchers due to its ease of visual representation. Same segmentation algorithms work differently for different applications, which makes them widely applicable. For instance, the same kMeans algorithm produces unique and efficient results when applied to areas like plant disease segmentation, medical resonance imaging (MRI), breast cancer detection, etc. A segmentation algorithm is said to be effective if the extracted regions of interest (ROI) match the expected regions of interest. A difference between the expected and the extracted ROI images is termed as minimum mean squared error (MMSE). This MMSE must be minimized in order to improve efficiency of the segmentation algorithm. In order to reduce MMSE various algorithms have been proposed by researchers over the years, the performance of these algorithm is heavily dependent on the application context. Modern day imaging systems can work effectively only if a hybrid combination of these algorithms is used. Thus, it is becoming increasingly difficult for researchers and imaging system designers to select highly effective application specific segmentation algorithms for their deployments. In order to solve this issue, this text reviews various state-of-the-art image segmentation algorithms and compares them in terms of statistical parameters like MMSE, peak signal to noise ratio (PSNR), delay of segmentation, etc. This will assist researchers and imaging system designers to select best algorithms for their given applications, and thereby reduce the time needed to design these systems. This will also assist designers to further enhance the system performance by selecting application specific image segmentation algorithms.