An incompressible fluid flow model with mutual information for MR image registration

Leo Tsai; Herng-Hua Chang

doi:10.1117/12.2005441

Abstract

Image registration is one of the fundamental and essential tasks within image processing. It is a process of determining the correspondence between structures in two images, which are called the template image and the reference image, respectively. The challenge of registration is to find an optimal geometric transformation between corresponding image data. This paper develops a new MR image registration algorithm that uses a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid flow governed by the nonlinear Navier-Stokes partial differential equation (PDE). We replace the pressure term with the body force mainly used to guide the transformation with a weighting coefficient, which is expressed by the mutual information between the template and reference images. To solve this modified Navier-Stokes PDE, we adopted the fast numerical techniques proposed by Seibold1. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information weight reaches a prescribed threshold. We applied our approach to the BrainWeb and real MR images. As consistent with the theory of the proposed fluid model, we found that our method accurately transformed the template images into the reference images based on the intensity flow. Experimental results indicate that our method is of potential in a wide variety of medical image registration applications.

Citation Download Citation

Leo Tsai, Leo Tsai, Herng-Hua Chang, Herng-Hua Chang, "An incompressible fluid flow model with mutual information for MR image registration", Proc. SPIE 8661, Image Processing: Machine Vision Applications VI, 86610T (6 March 2013); doi: 10.1117/12.2005441; https://doi.org/10.1117/12.2005441

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