A medical image registration method based on quantum evolutionary computation and b-spline transformation

Abstract

The invention discloses a medical image registration method based on quantum evolutionary computation and B spline conversion, and mainly aims to solve the problems that in the prior art, the matching degree is low, and the registration preprocessing process is complex. The method includes the steps that images are read in, and relevant parameters are initialized; quantum evolutionary computation populations are generated at random; registration parameters corresponding to individuals in the populations are calculated; three times of B spline function conversion is conducted on the floating images according to the registration parameters to acquire the converted floating images; normalization mutual information values between the converted floating images and a reference image are computed, a local generation optimal registration parameter is looked for, and quantum revolution door update is conducted on all the individuals in a local generation population; quantum regrouping operation is conducted through the updated population; circulation conditions are controlled, and the registration images and the registration parameters are output. Compared with a method based on a genetic algorithm, a better registration result can be acquired, registration time is shortened, and the method can be used for registering the medical images.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to medical image registration, in particular to a new image registration method based on quantum evolutionary calculation and B-spline transformation, which can be used for medical image fusion. Background technique [0002] Medical images can be divided into X-ray images, US images, CT images, MRI images, PET images, fMRI images, SPECT images, etc. according to different acquisition principles and methods. Among them, according to the different information it represents, it is mainly divided into anatomical structure image and functional image. This image contains the detailed anatomical structure of the organ and the metabolic function of the organ respectively. Both types of information are equally important for medical diagnosis, disease monitoring, and treatment guidance. Therefore, it is particularly important to fuse the medical images of the same patient at different ...

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Problems solved by technology

In YangChen, ZhengQin, MingyueSong.Acontinuousmedicalimageregistrationapproachbasedonimagesegmentationandgeneticalgorithm.ICIE-CS,2009,1:4, YangChen et al. used genetic algorithm in medical image registration, but the genetic algorithm has a long code length, resulting in relatively large space consumption, and It will still converge to the local extremum, causing the algorithm to end early before reaching the global optimal value, affecting the registration effect

HongkuiXu et al. used image shape features to achieve image registration in HongkuiXu, MingyanJiang, MingqiangYang.AnImageRegistrationmethodcombiningfeatureconstraintwithmultilevelStrategy, but complex steps are required to extract image shape features, and due to the complexity of non-rigid tissue structures, some interfaces are not very obvious and difficult Correctly extract shape features

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