Abstract
Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal.
Highlights
Magnetic Resonance Imaging (MRI) technique plays more and more important roles in the study of brain structure and its function because it offers amount of reliable information by a non-invasive approach [1]
We evaluate the performance of our proposed registration algorithm on two real datasets of Laboratory of Neuro Imaging (LONI) LPBA40 MR images and IXI MR images
In order to test our registration algorithm, we compare with the Diffeomorphic Demons and Symmetric-log-Demons algorithms on a public LONI dataset, which is widely used for testing registration algorithms
Summary
Magnetic Resonance Imaging (MRI) technique plays more and more important roles in the study of brain structure and its function because it offers amount of reliable information by a non-invasive approach [1]. Shen and Davatzikos proposed the HAMMER algorithm based on the process of feature detection and hierarchical deformation mechanism [6] and the further improved version, named TPS-HAMMER [7] by introducing thin-plate spline and softassign techniques [8] Sorzano and his coauthors used the B-Spline method to approximate the nonlinear deformation [9] while Rohde and his coauthors adopted the radially symmetric basis [10]. Inspired by the solution of Diffeomorphic Demons, we calculate the closed form of velocity at each iteration and design a novel nonlinear registration algorithm, in which a multi-scale strategy and a step-by-step warping process [31] are used.
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