A Generic Framework of Integrating Segmentation and Registration

We propose an integrated framework that can simultaneously segment and register a set of medical images using a geometric active model and a pseudo-likelihood method. First, we segment given medical volume data using a fast matching and geometric deformable model and extract a surface of an object from the segmented volume data. Second, we use the hidden Markov random field model and the pseudo-likelihood method to statistically model the intensity distribution of each voxel at the surface region. We adopt the Bernoulli probability model to formulate a prior distribution of the labeling variable for the transformed voxels. The Gaussian mixture model is taken as a probability distribution function for the intensity of the transformed voxel. We use the deterministic annealing EM (DAEM) algorithm to get the proper estimators for the parameters of the complete-data log likelihood function. Then, we define a new registration measure with the maximization function, called Q-function, obtained by the DAEM algorithm. We evaluate the precision of the proposed approach by comparing the registration traces of our measure with other measures such as Mutual Information or Cross Correlation for the original image and its transformed image with respect to translation and rotation. The experimental results show that our method has great potential power to segment and register various medical images given by different modalities.