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

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...

AI Technical Summary

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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