Markov random field model and non-local prior based image registration method

Abstract

The invention relates to a Markov random field model and non-local prior based image registration method which comprises the following steps of: (1) respectively reading in a target image and a floating image which are to be registered; (2) calculating the quadratic sum of differences of the target image and the floating image which are to be registered, and using the mean square distance of the two images as a similarity measure; (3) calculating non-local prior information of a displacement field as a regular term to carrying out smooth constraint on the displacement field; (4) adding the similarity measure and the non-local prior information to establish a Markov random field model, and converting registration into a question for solving for the minimum of an energy function of the Markov random field; (5) solving for the minimum of the energy function by adopting a sequence weighted tree information transfer algorithm; and (6) searching the minimum of the target function which is the energy function of the Markov random field, and when the energy function of the Markov random field is the minimum, finishing registering. The method has the advantages of high registration precision and strong robustness under the conditions of lower image space resolution, noise influences, and the like.

Description

technical field [0001] The invention relates to an image registration method, in particular to an image registration method based on a Markov random field model and a non-local prior. Background technique [0002] Image registration is an important aspect of the application of modern image processing technology. It refers to the spatial geometric transformation of two or more images at different times, different fields of view, and different imaging modes, so that pixels representing the same position and structure or Voxels can be geometrically matched and corresponding. The main purpose of image registration is to remove or suppress the geometric inconsistency between the image to be registered and the reference image, including translation, rotation, scaling and elastic deformation. It is a key step in image analysis and processing, and a necessary prerequisite for image comparison, data fusion, change analysis, and target recognition. Registration technology is mainly ...

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Problems solved by technology

However, the method of mutual information does not consider the spatial and directional information of the image, and the algorithm will fall into local extremum when the spatial resolution of the image is low and there is noise influence.

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