A Survey of Image Segmentation Algorithms Based on Expectation-Maximization

Abstract

Medical image segmentation plays an important role in one of the most challenging fields of engineering. Imaging modality provides detailed information about anatomy. It is also helpful in the finding of the disease and its progressive treatment. More research and work on it has enhanced more effectiveness as far as the subject is concerned. Different methods are used for medical image segmentation such as Clustering methods, Thresholding method, Classifier, Region Growing, Deformable Model, Markov Random Model etc. The main purpose of this survey is to provide a comprehensive reference source for the researchers involved in Expectation-Maximization based medical image processing. There are different types of Expectation- Maximization algorithms for medical image. Their advantages and disadvantages are discussed.