Image registration by normalized mapping
Neurocomputing
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Non-rigid image registration is widely used in medical image analysis and image processing. It remains a challenging research problem due to its smoothness requirement and high degree of freedoms in the deformation process. In [1], a method is proposed to solve non-rigid image registration via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement label (vector) is assigned to each pixel in the source image to indicate the corresponding position in the floating image. The whole system is then optimized by using the graph-cuts algorithm via alpha-expansions [2]. As the initial point is not required for the graph-cuts algorithm, the method proposed by [1] is a single-level registration. In this paper, rather single-level, we enable multi-level non-rigid image registration using graph-cuts by passing the deformation field of the current resolution level to the successive finer one. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. Therefore, the speed of the registration process is improved. We compare our results with the original single-level version, DEMONS and FFD. It is found that our method improves the speed of non-rigid image registration by 50% and meanwhile maintains similar robustness and registration accuracy with the single-level version.