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An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

Lei Ling1, Lijun Huang2, Jie Wang2, Li Zhang2, Yue Wu2, Yizhang Jiang1, Kaijian Xia2,3,*

1 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China
2 Department of Scientific Research, Changshu Hospital Affiliated to Soochow University, Changshu, 215500, China
3 China Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia

* Corresponding Author: Kaijian Xia. Email: email

(This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)

Computer Modeling in Engineering & Sciences 2023, 137(3), 2353-2379. https://doi.org/10.32604/cmes.2023.028828

Abstract

In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum; the clustering effect is poor for brain MR images with unclear boundaries and noise effects. To address these problems, a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed, which combines the generalized noise technique, relaxes the equational weight constraint in the objective function as the boundary constraint, and uses a genetic algorithm as a method to optimize the initialized clustering center. The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center. The experiment verifies the robustness of the algorithm, as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.

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Cite This Article

APA Style
Ling, L., Huang, L., Wang, J., Zhang, L., Wu, Y. et al. (2023). An improved soft subspace clustering algorithm for brain MR image segmentation. Computer Modeling in Engineering & Sciences, 137(3), 2353-2379. https://doi.org/10.32604/cmes.2023.028828
Vancouver Style
Ling L, Huang L, Wang J, Zhang L, Wu Y, Jiang Y, et al. An improved soft subspace clustering algorithm for brain MR image segmentation. Comput Model Eng Sci. 2023;137(3):2353-2379 https://doi.org/10.32604/cmes.2023.028828
IEEE Style
L. Ling et al., "An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation," Comput. Model. Eng. Sci., vol. 137, no. 3, pp. 2353-2379. 2023. https://doi.org/10.32604/cmes.2023.028828



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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