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The image signal is represented by using the atomic of image signal to train an over complete dictionary and is described as sparse linear combinations of these atoms. Recently, the dictionary algorithm for image signal tracking and decomposition is mainly adopted as the focus of research. An alternate iterative algorithm of sparse encoding, sample dictionary and dictionary based on atomic update process is K-SVD decomposition. A new segmentation algorithm of brain MRI image, which uses the noise reduction method with adaptive dictionary based on genetic algorithm, is presented in this paper, and the experimental results show that the algorithm in brain MRI image segmentation has fast calculation speed and the advantage of accurate segmentation. In a very complicated situation, the results show that the segmentation of brain MRI images can be accomplished successfully by using this algorithm, and it achieves the ideal effect and has good accuracy.

The technology of brain MRI image segmentation is the key of clinical diagnosis of brain medicine, and brain magnetic resonance image segmentation is an important step in medical diagnosis, which can not only be used to estimate and detect the lesion area, but also is an important basis for doctors to carry out the operation and make the diagnosis plan. So far, the main methods of brain MRI image segmentation are mainly based on classification method, filtering method and so on, and there are some disadvantages of the corresponding segmentation regions, for example, the inaccuracy of narrow focus area segmentation, sensitive to the image noise and other issues. Especially in the case of complex problems, some algorithms are simply not working. This paper presents a new segmentation algorithm of brain magnetic resonance imaging, using the method of combining adaptive dictionary and noise reduction based on genetic algorithm. The genetic algorithm does not require describing all of the features of the problems beforehand, and can also result in robust solutions to complex problems. But it has many disadvantages, because of these shortcomings, which lead to the deviation in medical image segmentation, and even wrong segmentation.

In this paper, on the basis of the traditional population, it adds a sub population, so that the two populations can result in more individuals and overcome the instability through competition. Numerical experiments show that the algorithm proposed in the brain MRI medical image segmentation application has fast calculation speed and accurate segmentation characteristics. The results of synthetic image segmentation show that the proposed algorithm which is better than the traditional genetic algorithm, has better robustness and can eliminate the interference of noise. When the noise pollution is more serious, the segmentation advantage of this algorithm can be better reflected.

K-SVD is given a set of signals in strict constraints under the conditions of the actual training [

1) Purpose: Find an optimal dictionary sparse representation

2) Initialization: Create a dictionary

3) Repeat the following steps until the stop condition is met:

a) Sparse coding: Calculate the following formula

b) Password update procedure: Each column of

1) Define a set of atomic samples

2) Calculate all error matrixes

3) Limit

4) Decompose

5)

The algorithm uses SVD to update the coefficients of the corresponding algorithm [

In

The results show that the adaptive dictionary denoising is an effective dictionary learning algorithm [

The evolutionary process of nature is the process of continuous iteration of the basic factors of reproduction, selection, crossover and mutation, so it can be regarded as an optimization problem to solve the optimal value of the process [

Two different individuals

The similarity between all individuals is found by using Hamming distance. Hamming distance is defined as follows:

where

In the dual population genetic algorithm, it is usually to introduce good individuals and eliminate the bad individuals. In this paper, the fitness of individuals in the population is arranged from large to small, and the competition among populations is accomplished by similarity comparison.

Through the calculation of Hamming distance, similar individuals will cross with other populations, then compute the fitness of function and select the dominant population. At the same time, in order to make the diversity of the form of the population, the sub population will regenerate a new individual.

The improvement of double population genetic algorithm realizes the segmentation of medical image [

and:

All the models are tested by MATLAB R2014a software in the Intel CPU Core i5-4590 @ 3.30 GHz, 8 GB of memory on the computer. Parameters of genetic algorithm: population has 30 individuals, mutation probability

From the visual point of view, in the results of brain MRI medical image

segmentation, the improved algorithm based on dictionary learning [

In the experiment, the initial population contains 40 individuals and the crossover rate is changing. Mutation probability

An improved dual population genetic algorithm, which is proposed to solve the problem of double population genetic algorithm in the paper, is stable and overcomes the premature phenomenon by experimental results; however, it is not robust to noise. In the brain MRI medical image, the segmentation results, which are produced by a noise reduction method based on adaptive dictionary learning that is introduced in the dual population improved genetic algorithm, show that the proposed algorithm has higher stability, more accurate segmentation, but also greatly reduces the computational complexity of genetic algorithm. Through the study of the brain MRI image segmentation results, this algorithm

has better portability in practical applications, and is more easy to popularize.

Cao, X.Q., Miao, J.Q. and Xiao, Y. (2017) Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning. World Journal of Engineering and Technology, 5, 90-96. https://doi.org/10.4236/wjet.2017.51008