Automatic Segmentation of Cerebral Computerized Tomography Based on Parameter-Limited Gaussian Mixture Model

This paper realizes a new method to segment intracranial structure from series of cerebral computerized tomography (CT) automatically. Firstly, a region growing and morphology based approach is developed to extract intracranial structures from series cerebral computerized tomography with the knowledge of anatomy, and then focusing on the problems of parameter initialization of the expectation maximization (EM) algorithm, an improved EM algorithm based on Parameter- Limited Gaussian Mixture Model is presented to segment intracranial structures successfully. Experiment shows that this method is successful on all cerebral computerized tomography from bottom to top part of cerebra.