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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy Clustering Algorithm
Yunlan Zhao1, Zhiyong Huang1, Hangjun Che2
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Abstract:
In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity.
Insights
This study introduces a novel weighted multitask fuzzy C-means (WMT-FCM) algorithm for brain magnetic resonance imaging (MRI) segmentation. The method enhances accuracy by leveraging multitask learning to address noise and inhomogeneity in MRI scans.
Area of Science:
- Medical Imaging
- Computer Vision
- Artificial Intelligence
Background:
- Brain magnetic resonance imaging (MRI) image segmentation is crucial for medical diagnosis and treatment planning.
- Traditional segmentation algorithms struggle with inherent MRI image challenges like noise and grayscale inhomogeneity.
- Existing methods require further improvements in accuracy and stability for clinical applications.
Purpose of the Study:
- To propose a novel brain MRI image segmentation algorithm to enhance segmentation accuracy.
- To address the limitations of traditional segmentation methods in handling noisy and inhomogeneous MRI data.
- To improve the reliability of segmentation results for better clinical decision-making.
Main Methods:
- A novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering is proposed.
- Multitask learning strategy is introduced into FCM to extract shared information across different segmentation tasks.
- An adaptive task weight learning mechanism is developed, leading to a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm.
Main Results:
- The proposed WMT-FCM algorithm demonstrated more accurate and stable segmentation results compared to existing methods.
- The algorithm effectively utilizes both shared information among tasks and individual task-specific information.
- Experimental results on simulated MRI images (McConnell BrainWeb) validated the algorithm's performance under various noise and intensity inhomogeneity conditions.
Conclusions:
- The developed WMT-FCM algorithm offers a significant improvement in brain MRI image segmentation accuracy and stability.
- The multitask learning approach effectively handles the complexities of MRI data, including noise and inhomogeneity.
- This novel method provides a more robust foundation for medical diagnosis and clinical treatment planning based on MRI segmentation.
