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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field
Mingsheng Chen1, Qingguang Yan2, Mingxin Qin1
1a College of Biomedical Engineering , Third Military Medical University , Chongqing , China.
Background And Objective:
Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MRI images. A precise and anti-noise segmentation of MRI images is desired in modern medical image diagnosis.
Methods:
This paper presents a segmentation of MRI images which combines fuzzy clustering and Markov random field (MRF). In order to utilize gray level information sufficiently and alleviate noise disturbance, fuzzy clustering is carried out on the original image and the coarse scale image of multi-scale decomposition. The spatial constraints between neighboring pixels are modeled by a defined potential function in the MRF to reduce the effect of noise and increase the integrity of segmented regions. Spatial constraints and the gray level information refined by Fuzzy C-Means (FCM) algorithm are integrated by maximum a posteriori Markov random field (MAP-MRF). In the proposed method, the fuzzy clustering membership obtained from the original image and the coarse scale image is integrated into the single-site clique potential functions by MAP-MRF. The defined potential functions and the distance weight are introduced to model the neighborhood constraint with MRF.
Results:
The experiments are carried out on noised synthetic images, simulated brain MR images and real MR images. The experimental results show that the proposed method has strong robustness and satisfying performance. Meanwhile the method is compared with FCM, FGFCM and FLICM algorithms visually and statistically in the experiments. In the comparison, the proposed method has achieved the best results. In the statistical comparison, the proposed method has an average similarity index of 36.8%, 33.7%, 2.75% increase against FCM, FGFCM and FLICM.
Conclusions:
This paper proposes a MRI segmentation method combining fuzzy clustering and Markov random field. The method is tested in the noised image databases and comparison experiments, which shows that it is a precise and robust MRI segmentation method.
Insights
This study introduces a novel MRI segmentation technique combining fuzzy clustering and Markov random fields. The method offers precise and robust image segmentation, outperforming existing algorithms in noisy conditions.
Area of Science:
- Medical Imaging
- Computer Vision
- Image Analysis
Background:
- Image segmentation is crucial for computer-aided MRI analysis.
- Noise significantly degrades the performance of current MRI segmentation methods.
- Precise and noise-resilient segmentation is essential for modern medical diagnosis.
Purpose of the Study:
- To develop a precise and anti-noise segmentation method for MRI images.
- To improve the robustness of image segmentation in the presence of noise.
- To enhance the integrity of segmented regions in medical image analysis.
Main Methods:
- Combines fuzzy clustering (Fuzzy C-Means) with Markov Random Fields (MRF).
- Utilizes multi-scale decomposition and fuzzy clustering on original and coarse-scale images.
- Integrates spatial constraints via MRF potential functions and MAP-MRF for noise reduction.
Main Results:
- The proposed method demonstrates strong robustness and satisfying performance on synthetic, simulated, and real MRI data.
- Achieved superior results compared to FCM, FGFCM, and FLICM algorithms.
- Showed an average similarity index increase of 36.8%, 33.7%, and 2.75% over FCM, FGFCM, and FLICM, respectively.
Conclusions:
- A novel MRI segmentation method integrating fuzzy clustering and MRF is proposed.
- The method is validated on noisy image databases, proving its precision and robustness.
- This approach offers significant improvements for medical image segmentation tasks.
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