Medical image registration using sparse coding and belief propagation.

Recently, various medical imaging such as CT and MRI imaging has been used more and more widely in clinical and medical research. As a result, there is an increasing interest in accurately relating information in different images for diagnosis, treatment, and the sake of basic science. As images are typically acquired at different times and often by different modalities, registering (or aligning) one image with another is not a simple task in general and it success will affect the effectiveness and accuracy of all subsequent analysis. We propose an efficient medical image registration method based on sparse coding and belief propagation for medical CT imaging. We used 3-D image blocks as features, and then we employed sparse coding to find a set of candidate voxels. To select optimum matches, belief propagation was subsequently applied on these candidate voxels. The outcome of belief propagation was interpreted as probabilistic map between candidate voxels and source voxel. We compared with the state-of-the-art of medical image registration, MIRT and GP-Registration algorithm. Our objective results based on RMSE (Root Mean Square Error) are smaller than those from MIRT and GP-Registration. Our results also proved the effectiveness of our algorithm in registering reference image to source image.