A Shape-Based Approach to the Registration of Medical Imagery Using Gaussian Mixture Models
Jonghyun Park, Wanhyun Cho, Soonyoung Park, Myungeun Lee, Sunworl Kim, Chang-Bu Jeong, Junsik Lim, Guee-Sang Lee · 2009
The problems of segmentation and registration are traditionally approached separately; yet the accuracy of one is of great importance in influencing the accuracy of the other. We propose a new method, using shape information and a statistical model, to address the problem of multimodality medical image registration. Using the approach presented in this paper, we apply a Q-function to measure the statistical dependence or information redundancy between the probability distributions of corresponding voxels from the region of interest in both images. We define a new registration measure with a Q-function obtained by a Gaussian mixture model (GMM) based on an Expectation-maximization (EM) algorithm. The Q-function is assumed to be maximal if the two images for the registration are geometrically aligned. Using the registration traces based on the Q-function, we evaluate the precision of the proposed approach between MR images and CT images. The experimental results show that our method can be very successful in registering various medical images that use different modalities.