Image registration method based on mixed mutual information and improved particle swarm optimization

Abstract

The invention provides an image registration method based on mixed mutual information and improved particle swarm optimization. The image registration method comprises the following steps that mutual information of a floating image and a reference template image is calculated by Renyi entropy; the population size, the maximum number of steps of algorithm iteration, a nonlinear adjustment parameter n and the minimum threshold are set; the new speed and position of each particle are calculated according to the speed updating and position updating formula of a PSO algorithm so that the local optimal solution Pid and the global optimal solution Pgd are updated; local optimization searching is performed on the global optimal solution Pgd with shannon entropy acting as similarity measure so that global optimal solution Rid is calculated; the global optimal solution Rid and the preset minimum threshold Fmin are compared until iteration ends or algorithm registration fails and then the algorithm ends; and mapping of the gray value of each pixel of the transformed images is performed. Compared with the methods in the prior art, the image registration method is high in calculation speed, short in registration time and more accurate in registration parameter.

Description

technical field [0001] The invention relates to the technical field of medical image registration, in particular to an image registration method based on mixed mutual information and improved particle swarm optimization. Background technique [0002] Image registration technology plays an important role in the field of medical image processing. Medical image registration is mainly divided into feature-based registration and voxel-based registration, among which the voxel-based registration method based on mutual information is the most widely used. The voxel registration method based on mutual information refers to the registration of the gray information of the image itself, using the similarity measure as the basic registration index to calculate the similarity between the two images under different transformations. However, this method takes a long time for registration, a large amount of calculation, and high computational complexity. The feature-based registration nee...

AI Technical Summary

Problems solved by technology

In summary, there are some problems in the current voxel registration method based on mutual information: long registration time, large amount of calculation, high computational complexity; low degree of automation, and the intervention of experienced doctors or experts is required to complete the registration. Accurate; although the registration speed is fast, it is easy to cause mis-matching

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