Automatic 3D Image Segmentation Using Adaptive k-means on Brain MRI

The aim of this paper is to present a fully automatic adaptive k-means segmentation algorithm for MR Images in a 3D space. We model the gray scale values of the 3D image with a White Gaussian Process and superimpose a prior model on the region process in the form of Markov Random Field. These assumptions require the use of estimators for the parameters of the two processes. This has been carried out using decreasing size windows. The Hammersley-Clifford theorem allows us to model the region process in term of a Gibbs Distribution. The Gibbs parameter β is estimated using a correlation-based technique. The segmentation is obtained maximizing the a posterior density function using an Iterated Conditional Modes technique. The proposed algorithm is fully automatic, i.e. all the parameters of the model are estimated within the segmentation process.