Ling, Lei and Huang, Lijun and Wang, Jie and Zhang, Li and Wu, Yue and Jiang, Yizhang and Xia, Kaijian (2023) An improved soft subspace clustering algorithm based on particle swarm optimization for MR image segmentation. Interdisciplinary Sciences-Computational Life Sciences, 15 (4). pp. 560-577. ISSN 1913-2751, DOI https://doi.org/10.1007/s12539-023-00570-2.
Full text not available from this repository.Abstract
Soft subspace clustering (SSC), which analyzes high-dimensional data and applies various weights to each cluster class to assess the membership degree of each cluster to the space, has shown promising results in recent years. This method of clustering assigns distinct weights to each cluster class. By introducing spatial information, enhanced SSC algorithms improve the degree to which intraclass compactness and interclass separation are achieved. However, these algorithms are sensitive to noisy data and have a tendency to fall into local optima. In addition, the segmentation accuracy is poor because of the influence of noisy data. In this study, an SSC approach that is based on particle swarm optimization is suggested with the intention of reducing the interference caused by noisy data. The particle swarm optimization method is used to locate the best possible clustering center. Second, increasing the amount of geographical membership makes it possible to utilize the spatial information to quantify the link between different clusters in a more precise manner. In conclusion, the extended noise clustering method is implemented in order to maximize the weight. Additionally, the constraint condition of the weight is changed from the equality constraint to the boundary constraint in order to reduce the impact of noise. The methodology presented in this research works to reduce the amount of sensitivity the SSC algorithm has to noisy data. It is possible to demonstrate the efficacy of this algorithm by using photos with noise already present or by introducing noise to existing photographs. The revised SSC approach based on particle swarm optimization (PSO) is demonstrated to have superior segmentation accuracy through a number of trials; as a result, this work gives a novel method for the segmentation of noisy images.
| Item Type: | Article |
|---|---|
| Funders: | Suzhou Key Supporting Subjects [Grant No: SZFCXK202147], Changshu Science and Technology Program [Grant No: CS202015; CS202246], Changshu City Health and Health Committee Science and Technology Program [Grant No: csws201913], National Natural Science Foundation of China (NSFC) [Grant No: 62171203] |
| Uncontrolled Keywords: | Soft subspace clustering (SSC); Particle swarm optimization (PSO); Generalized noise clustering; Image segmentation; Medical MR image |
| Subjects: | Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology |
| Divisions: | Faculty of Engineering > Department of Biomedical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Oct 2025 13:00 |
| Last Modified: | 06 Oct 2025 13:00 |
| URI: | http://eprints.um.edu.my/id/eprint/48291 |
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