Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field

Jianhua Song; Lei Yuan; Jianhua Song; Lei Yuan

doi:10.3934/mbe.2022089

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 2: 1891-1908. doi: 10.3934/mbe.2022089

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Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field

Jianhua Song ^{1,2
,
,},
Lei Yuan ²

1.
The Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou, 363000, China
2.
College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China

Received: 24 October 2021 Accepted: 14 December 2021 Published: 20 December 2021

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.
- Magnetic resonance imaging,
- brain tissue segmentation,
- fuzzy c-means clustering,
- Markov random field,
- non-local constraints
Citation: Jianhua Song, Lei Yuan. Brain tissue segmentation via non-local fuzzy c-means clustering combined with Markov random field[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 1891-1908. doi: 10.3934/mbe.2022089

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Abstract

The segmentation and extraction of brain tissue in magnetic resonance imaging (MRI) is a meaningful task because it provides a diagnosis and treatment basis for observing brain tissue development, delineating lesions, and planning surgery. However, MRI images are often damaged by factors such as noise, low contrast and intensity brightness, which seriously affect the accuracy of segmentation. A non-local fuzzy c-means clustering framework incorporating the Markov random field for brain tissue segmentation is proposed in this paper. Firstly, according to the statistical characteristics that MRF can effectively describe the local spatial correlation of an image, a new distance metric with neighborhood constraints is constructed by combining probabilistic statistical information. Secondly, a non-local regularization term is integrated into the objective function to utilize the global structure feature of the image, so that both the local and global information of the image can be taken into account. In addition, a linear model of inhomogeneous intensity is also built to estimate the bias field in brain MRI, which has achieved the goal of overcoming the intensity inhomogeneity. The proposed model fully considers the randomness and fuzziness in the image segmentation problem, and obtains the prior knowledge of the image reasonably, which reduces the influence of low contrast in the MRI images. Then the experimental results demonstrate that the proposed method can eliminate the noise and intensity inhomogeneity of the MRI image and effectively improve the image segmentation accuracy.

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