A multi-resolution medical image registration method based on quantum behaviors particle swarm algorithm

Abstract

The invention relates to a multi-resolution medical image registration method based on the quantum-behaved particle swarm optimization, which is characterized in that the method comprises the following step of: firstly removing the backgrounds of the two images to be registered to keep the images off the interference of noise; next obtaining the images with low resolution from the two background-removed images via the wavelet transform, using the low resolution to be the object, taking the normalized mutual information as the objective function, using the quantum-behaved particle swarm optimization, then the images with the high resolution to be the object, and obtaining the rotation amount and the translation amount between the two images to be registered to finish the image registration by using the Powell method. With solving a plurality of local extrema based on the objective function of the mutual information, the invention greatly improves the registration precision and speed, reaching up to the sub-pixel level; and is widely applicable to the fields of the image discrimination of the clinical diagnosis, the framing of the radiation treatment, the image guided surgical, etc.

Description

technical field [0001] The invention relates to a multi-resolution medical image registration method based on quantum behavior particle swarm algorithm, which can be specifically used to solve many local extremum problems existing in an objective function using mutual information. Image localization in radiotherapy and image guidance in surgery have a wide range of applications. Background technique [0002] In the prior art, the registration technology of medical images is an important branch of medical image processing developed in the 1990s. It is a basic task of medical image processing and is of great significance for clinical diagnosis and treatment. attention of the engineering community. Medical image registration refers to seeking a (or a series of) spatial transformation for a medical image to make it spatially consistent with the corresponding points on another medical image. This consistency refers to the same anatomy on the human body. Points have the same spa...

AI Technical Summary

Problems solved by technology

For example, both the Powell method and the genetic algorithm are direct optimization methods that do not require derivatives, but the convergence speed of the genetic algorithm is slow, while the optimization speed of the Powell method is fast, but it is easy to fall into a local optimum, while the particle swarm optimization algorithm has a fast convergence speed. , but since it is not a global optimization algorithm, it is also easy to fall into a local optimal solution

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