Brain MR image registration method based on anisotropic optical flow field and debiasing field

Abstract

The invention provides an anisotropic optical flow field and deskew field-based brain MR (magnetic resonance) image registration method, and discloses an optical flow field model-based image registration and biased field recovery coupling model. Aiming at the defect that a biased field can easily cause low registration accuracy of the traditional registration model, the anisotropic optical flow field and the deskew field are combined, and are brought into a unified variational framework, thus the two can supplement each other, and in view of the fact that global regular terms of a Horn model are incapable of guiding the optical flow precisely due to the lack of image information and the movement is insufficient, the image structure information is introduced to regulate the optical flow field, so as to get smooth and accurate optical flow information. According to the anisotropic optical flow field and deskew field-based brain MR image registration method, the image gradation non-uniform field can be restored while registration is performed, and the influence of the biased field is reduced; the image structure information is introduced to reduce the registration results fuzzy degree and retain the image structure information, the integrity of boundary structure information is guaranteed, and further recovery of real images is facilitated. The registration precision is greatly improved, and the robustness is good.

Description

technical field [0001] The invention belongs to the technical field of image registration, in particular to an improved brain MR image registration method. Background technique [0002] Image registration has a wide range of applications in remote sensing image processing, computer vision, medical image analysis and other fields. In the field of medical images, it is the basis for information fusion of different medical images. Registration aims to seek a spatial transformation for specifying an image so that it achieves spatial consistency with corresponding points on another medical image. have the same spatial location. Image registration methods are divided into rigid and non-rigid, because the change of human tissue is non-rigid and nonlinear, so the non-rigid registration method has a better registration effect. [0003] Currently, non-rigid registration methods are mainly divided into two categories: pixel-based methods and feature-based methods. Pixel-based metho...

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Problems solved by technology

Aiming at the disadvantage that the offset field easily leads to low registration accuracy of the traditional registration model, the present invention combines the debiasing field and the optical flow field into a unified variational framework, so that the two complement each other, aiming at the overall Horn model Due to the lack of image information, the regularization item cannot accurately guide the optical flow movement. The present invention introduces image structure information to regularize the optical flow field, thereby obtaining smooth and accurate optical flow information.

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