Image segmentation is the most critical function in image analysis and processing. Results of segmentation fundamentally affect all subsequent image analysis processes such as representation and description of objects, measurement of features, and even higher-level tasks such as classification of objects. Image segmentation is therefore the most essential and crucial process for facilitating the delineation, characterization and visualization of regions of interest in any medical image. The radiologist`s manual segmentation of the medical image is not just a tedious and time-consuming technique, also not very accurate, especially with the increasing medical imaging modalities and the unmanageable quantity of medical images that need to be examined. It is therefore necessary to review current image segmentation methodologies using automated algorithms that are accurate and require as little user interaction as possible, especially for medical images. In the segmentation process, it is necessary to delineate and extract the anatomical structure or region of interest so that it can be viewed individually. In this paper, we are projecting the important place of image segmentation in decision-making information extraction and deliberating upon current techniques which are used in medical imaging and discussing about various advancements in this research field.

Medical Imaging, Segmentation, Watershed Transform (WT), Expectation Maximization (EM), Level Set Method (LSM), Genetic Algorithms (GA), Artificial Neural Networks (ANN)

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