STATISTICAL APPROACH TO X-RAY CT IMAGING AND ITS APPLICATIONS IN IMAGE-ANALYSIS .2. A NEW STOCHASTIC MODEL-BASED IMAGE SEGMENTATION TECHNIQUE FOR X-RAY CT IMAGE

Based on the statistical properties of X-ray CT imaging given in Part I of this paper, an unsupervised stochastic model-based image segmentation technique for X-ray CT image is presented. This technique utilizes the finite normal mixture distribution and the underlying Gaussian random field (GRF) as the stochastic image model. The number of image classes in the observed image is detected by information theoretic criteria (AIC or MDL). The parameters of the model are estimated by expectation-maximization (EM) and classification-maximization (CM) algorithms. Image segmentation is performed by Bayesian classifier. Results from the use of simulated and real X-ray CT image data are presented to demonstrate the promise and effectiveness of the proposed technique.