The Community for Technology Leaders
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
ISBN: 978-0-7695-3507-4
pp: 213-217
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.

J. Lim et al., "A Shape-Based Approach to the Registration of Medical Imagery Using Gaussian Mixture Models," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 213-217.
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