Integrating Segmentation Information for Improved MRF-Based Elastic Image Registration
In this paper, we propose a method to exploit segmentation information for elastic image registration using a Markov-random-field (MRF)-based objective function. MRFs are suitable for discrete labeling problems, and the labels are defined as the joint occurrence of displacement fields (for registration) and segmentation class probability. The data penalty is a combination of the image intensity (or gradient information) and the mutual dependence of registration and segmentation information. The smoothness is a function of the interaction between the defined labels. Since both terms are a function of registration and segmentation labels, the overall objective function captures their mutual dependence. A multiscale graph-cut approach is used to achieve subpixel registration and reduce the computation time. The user defines the object to be registered in the floating image, which is rigidly registered before applying our method. We test our method on synthetic image data sets with known levels of added noise and simulated deformations, and also on natural and medical images. Compared with other registration methods not using segmentation information, our proposed method exhibits greater robustness to noise and improved registration accuracy.