Brain part MRI image segmentation method

Abstract

The invention provides a brain part MRI image segmentation method. The brain part MRI image segmentation method is characterized in that a gray level image of a brain part MRI image to be segmented can be acquired; the gray values of different pixel points of the brain part MRI image can be used as the clustering centers, which are used to form the clustering center sets as the particles, and the optimization of the clustering center sets can be carried out by adopting the particle swarm optimization algorithm; every pixel point of the brain part MRI image belongs to the category having the maximum membership, and then the gray values of the pixel points of the same category are equal to the same gray value, and the brain part MRI image segmentation can be completed. The brain part MRI image segmentation method is advantageous in that according to the chaotic characteristic and the logic self-mapping function, the uniformly-distributed particle swarms can be initialized by adopting the logic self-mapping function, and then the quality of the initial solution, the stability of the PSO algorithm, the speed and the precision of the image segmentation can be improved; the chaotic searching can be carried out, when the particles are in the premature convergence state, and the premature convergence phenomenon caused by the stagnated state of the particles during the iteration process can be prevented, and the optimal solution in the range of the whole situation can be realized, and then the speed and the precision of the image segmentation can be improved.

Description

technical field [0001] The invention belongs to the field of medical image processing, in particular to a brain MRI image segmentation method. Background technique [0002] In recent years, the rapid development of computer technology, communication technology, and network technology has brought new vitality to Computer Aided Diagnosis (CAD). CAD utilizes the high efficiency of computer analysis and processing of medical images, saving a lot of manpower and material resources. , and can make objective and practical diagnostic results without being affected by human subjective consciousness, so as to cooperate with doctors to make accurate judgments on the condition. In medical image processing, medical image segmentation is a prerequisite for solving the clinical application of medical images. It is a complex and challenging task, especially the segmentation of human brain images. Brain segmentation is important for early diagnosis of brain tumors. Diseases such as Alzheime...

AI Technical Summary

Problems solved by technology

Currently the most widely used is the fuzzy C-means clustering (FuzzyC-Means, FCM) algorithm, which obtains the degree of membership of each pixel by iteratively optimizing the objective function with fuzzy parameters for image segmentation. It is very sensitive to the initial value and noise interference, and depends to a large extent on the selection of the initial clustering center. When the initial clustering center deviates seriously from the global optimal clustering center, it is easy to fall into the local optimal solution, which reduces the segmentation speed and performance. are affected

[0004] In recent years, some scholars have proposed to use particle swarm optimization (Particle Swarm Optimization, PSO) to optimize the initial clustering center, and the chaotic particle swarm optimization (CPSO) inspired by chaos theory has been applied to image segmentation, but these literatures The judgment of the premature phenomenon of particle swarms has not been proposed, and most of the chaotic models used are Logistic maps. However, the sequences generated by Logistic maps are extremely uneven and have poor stability.

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