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Medical image segmentation method based on quantum-behaved particle swarm cooperative optimization

A quantum particle swarm, medical image technology, applied in the field of collaborative quantum particle swarm optimization, can solve the problems of stagnant evolution, trapping, local optimal state, etc., achieve short time, improve segmentation accuracy, and increase the chance of jumping out of local optimal. big effect

Inactive Publication Date: 2012-06-13
XIDIAN UNIV
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Problems solved by technology

This method combines a quantum particle swarm algorithm with the traditional OTSU method for image segmentation. The optimization process is no longer an exhaustive search, so the time complexity of multi-threshold segmentation is reduced, but similar to other intelligent algorithms, this In the segmentation process of the current image segmentation method based on quantum particle swarm algorithm, there is still a shortcoming of stagnating evolution and falling into a local optimal state when the obtained segmentation results reach a certain level.

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  • Medical image segmentation method based on quantum-behaved particle swarm cooperative optimization
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  • Medical image segmentation method based on quantum-behaved particle swarm cooperative optimization

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[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0038] Step 1, read in the medical image, obtain the matrix, and obtain the minimum gray value and maximum gray value from the matrix;

[0039] Step 2, initialize the population: randomly generate an integer between the minimum gray value and the maximum gray value as the first dimension of the population individual, and randomly generate an integer between the first dimension and the maximum gray value as the first dimension of the population individual Two-dimensional, and so on, each dimension is randomly generated from the previous dimension obtained by initialization to the maximum gray value, and the initialization of all population individuals is completed, and the individual dimension ranges from 1 to 4. In the embodiment of the present invention, the dimen...

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Abstract

A medical image segmentation method based on quantum-behaved particle swarm cooperative optimization mainly solves the problem that increasing of categories in the prior art of image segmentation results in overlong segmentation time, local optimum of segmentation results and low segmentation precision within bearable time. The technical scheme includes step one, reading in medical images to obtain a matrix; step two, initializing population; step three, obtaining individual optimum and global optimum; step four, generating new individuals; step five, generating new individual optimum and global optimum; step six, judging whether the current iteration meets the maximum iteration or not, if yes, performing the step seven, if not, returning to the step four; step seven, performing image segmentation; and step eight, outputting segmented image matrix. The Monte Carlo method is used for multiple measurement during image segmentation threshold valuating, cooperation strategy is used for individuals obtained by multiple measurement, and accordingly the medical image segmentation method has the advantage of quickness in obtaining of ideal segmentation results and can be used for multi-threshold segmentation of medical images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a cooperative quantum particle swarm optimization method in the technical field of medical image segmentation. The invention can be used to segment medical images such as CT images, MRI images, B-ultrasound images, etc., so as to realize the comparison between the pathological images of organs and normal images, and then analyze the degree of pathological changes of organs. Background technique [0002] Medical image processing and analysis is a technology that uses computers to process and analyze images collected by medical imaging equipment, which can assist doctors in making more accurate diagnoses. The research content involved in medical image processing and analysis technology includes: medical image segmentation, medical image registration, 3D visualization, computer-aided diagnosis and telemedicine, etc. Among them, medical image segmentation is the basic...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 李阳阳相荣荣焦李成刘若辰公茂果马文萍尚荣华韩红
Owner XIDIAN UNIV
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