Skip to main content
SpringerLink
Log in

An Improved Soft Subspace Clustering Algorithm Based on Particle Swarm Optimization for MR Image Segmentation

  • Original research article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

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.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Xie CH, Chang JY, Xu WB (2014) Medical image denoising by generalised Gaussian mixture modelling with edge information. IET Image Proc 8(8):464–476. https://doi.org/10.1049/iet-ipr.2013.0202

    Article  Google Scholar 

  2. Hardaha S, Edla DR, Parne SR (2022) A survey on convolutional neural networks for MRI analysis. Wire Pers Commun. 128(2):1065–1085. https://doi.org/10.1007/S11277-022-09989-0

    Article  Google Scholar 

  3. Agrawal R, Gehrke JE, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD Rec 27(2):94–105. https://doi.org/10.1145/276305.276314

    Article  Google Scholar 

  4. Bi XW, Zhang XH, Wang SD, Zhang HF (2022) Entropy-weighted reconstruction adversary and curriculum pseudo labeling for domain adaptation in semantic segmentation. Neurocomputing 506:277–289. https://doi.org/10.1016/J.NEUCOM.2022.07.073

    Article  Google Scholar 

  5. Wang Q, Gong CY, Wang PH (2022) Clustering based on adaptive local density with evidential assigning strategy. Intel Data Anal 1:1–4. https://doi.org/10.3233/IDA-205670

    Article  Google Scholar 

  6. Wei TY, Wang XP, Li XN, Zhu SY (2022) Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking. Eng Appl Artif Intell 110:104672. https://doi.org/10.1016/J.ENGAPPAI.2022.104672

    Article  Google Scholar 

  7. Yang ZL, Wu A (2020) A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation. Neural Comp Appl. 32(16):12011–12031. https://doi.org/10.1007/s00521-019-04210-z

    Article  Google Scholar 

  8. Dai LL, Qi P, Lu H, Liu XH, Hua DZ, Guo XQ (2023) Image enhancement method in underground coal mines based on an improved particle swarm optimization algorithm. Appl Sci 13(5):3254–3254. https://doi.org/10.3390/APP13053254

    Article  CAS  Google Scholar 

  9. Liu C, Xie J, Zhao Q, Liu CQ (2019) Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment. Expert Syst Appl 1533(2):022052. https://doi.org/10.1016/j.eswa.2019.112827

    Article  Google Scholar 

  10. Chen YT (2020) Evaluation and Analysis of Electronic Information Industry Clustering Level Based on Soft Subspace Clustering Algorithm. J Phys Conf Series. 1533(2):022052. https://doi.org/10.1088/1742-6596/1533/2/022052

    Article  Google Scholar 

  11. Song JH, Zhang Z (2019) A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation. Information 10(2):74–74. https://doi.org/10.3390/info10020074

    Article  Google Scholar 

  12. Liu Z, Song YQ, Xie CH, Tang Z (2016) A new clustering method of gene expression data based on multivariate Gaussian mixture models. Sig Image Video Proce 10(2):359–368. https://doi.org/10.1007/s11760-015-0749-5

    Article  Google Scholar 

  13. Andono PN, Supriyanto C (2022) Keypoints class distribution based entropy for weighting scheme on image classification. J King Saud Univer Comp Inform Sci 34(10):9028–9038. https://doi.org/10.1016/J.JKSUCI.2022.08.027

    Article  Google Scholar 

  14. Zhang YD, Dong Z, Wang SH, Yu X, Gorriz JM (2020) Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Infor Fusion 64:149–187. https://doi.org/10.1016/j.inffus.2020.07.006

    Article  Google Scholar 

  15. Dave RN, Sen S (1998) Generalized noise clustering as a robust fuzzy c-M-estimators model. Piscataway. https://doi.org/10.1016/j.ins.2016.01.101

    Article  Google Scholar 

  16. AlSuwaidan L, Khan S, Almakk R, Baig A (2022) Swarm Intelligence algorithms for optimal scheduling for cloud-based fuzzy systems. Mathemat Prob Eng. 10(17):3183–3183. https://doi.org/10.1155/2022/4255835

    Article  Google Scholar 

  17. Arun M, Mario P (2021) Adversarial feature learning for improved mineral mapping of CRISM data. Icarus 355:114107–114107. https://doi.org/10.1016/j.icarus.2020.114107

    Article  Google Scholar 

  18. Roberto S, Oeslle L, Julia G, Roberto L (2018) An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement. Neuroimage 170:482–494. https://doi.org/10.1016/j.neuroimage.2017.08.021

    Article  Google Scholar 

  19. Alsghaier H, Akour M (2020) Software fault prediction using Whale algorithm with genetics algorithm. Software 51(5):1121–1146. https://doi.org/10.1002/SPE.2941

    Article  Google Scholar 

  20. Firman, T. & Achmad, F.A. .(2018).Clustering of Potency of Shrimp In Indonesia With K-Means Algorithm And Validation of Davies-Bouldin Index..(eds.)Proceedings of the International Conference on Science and Technology, Atlantis Press,730–733. https://doi.org/10.2991/ICST-18.2018.148

  21. Bagirov AM, Aliguliyev RM, Sultanova N (2023) Finding compact and well-separated clusters: Clustering using silhouette coefficients. Patt Recognit. 135:109144. https://doi.org/10.1016/J.PATCOG.2022.109144

    Article  Google Scholar 

Download references

Funding

This research was funded by Suzhou Key Supporting Subjects [(Health Informat-ics(No.SZFCXK202147))]; Changshu Science and Technology Program [No.CS202015, CS202246]; Changshu City Health and Health Committee Science and Technology Program [No. csws201913]; and the National Natural Science Foundation of China under Grant 62171203.

Author information

Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Lei Ling & Yizhang Jiang

  2. The Changshu Affiliated Hospital of Soochow University, Suzhou, 215500, Jiangsu, China

    Lijun Huang, Jie Wang, Li Zhang, Yue Wu & Kaijian Xia

  3. Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Kaijian Xia

Authors
  1. Lei Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yue Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yizhang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kaijian Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaijian Xia.

Ethics declarations

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ling, L., Huang, L., Wang, J. et al. An Improved Soft Subspace Clustering Algorithm Based on Particle Swarm Optimization for MR Image Segmentation. Interdiscip Sci Comput Life Sci 15, 560–577 (2023). https://doi.org/10.1007/s12539-023-00570-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-023-00570-2

Keywords

Search

Navigation