Unsupervised Fuzzy C-Means Segmentation with Adaptive Cluster Count Derived from Laplacian of Gaussian Edge Maps

  • Authors

    • Dr. Rajendra Vasantrao Patil Department of Computer Engineering, SSVPS Bapusaheb Shivajirao Deore College of Engineering, Dhule (MS), India
    • Deepak Yashwantrao Bhadane R. C. Patel College of Engineering and Polytechnic, Shirpur (MS)، India
    • Dr. Abhijit Dandavate Dhole Patil College of Engineering, Pune (MS)، India
    • Dr Priti Shende Department of Electronics and Telecommunication, Dr. D. Y. Patil Institute of Technology, Pune (MS)، India
    • Dr. Govind Mohanlal Poddar NES Gangamai College of Engineering, Nagaon, Dhule (MS)، India
    • Shravani R. Patil Department of Computer Engineering, SSVPS Bapusaheb Shivajirao Deore College of Engineering, Dhule (MS)، India
    https://doi.org/10.14419/ac56hj76

    Received date: June 15, 2025

    Accepted date: July 11, 2025

    Published date: July 22, 2025

  • Fuzzy C-Means; Image Segmentation; Adaptive Clustering; Laplacian of Gaussian; Edge Detection; Embedded Integration; Unsupervised ‎Learning.

  • Abstract

    This paper proposes an embedded integration framework for unsupervised image segmentation that combines the strengths of edge detection and soft clustering. Traditional segmentation algorithms, such as K-means, often rely on a manually defined number of clusters (k), which can lead to inconsistent and less effective results across different images. To overcome this limitation, we introduce a Laplacian of ‎Gaussian (LoG)-guided method that adaptively estimates the optimal number of clusters (C) based on structural edge information. The LoG ‎operator detects zero-crossings corresponding to object boundaries, followed by edge refinement through connectivity analysis, spurious ‎edge removal, and color-based grouping. This adaptively determined C is used as input to the Fuzzy C-Means (FCM) algorithm, which ‎offers soft clustering with improved handling of ambiguous or gradual region boundaries. Experimental evaluations on diverse images ‎show that the proposed LoG-FCM framework outperforms traditional K-means clustering in both visual quality and quantitative metrics. ‎Notably, it achieves higher values in Jaccard Index, Dice Coefficient, Precision, Recall, and Adjusted Rand Index, while reducing Variation ‎of Information and Hausdorff Distance. The results highlight the robustness, accuracy, and autonomous nature of the proposed segmentation approach‎.

  • References

    1. K. K. Brar et al., “Image segmentation review: Theoretical background and recent advances,” Inf. Fusion, vol. 114, p. 102608, Feb. 2025, https://doi.org/10.1016/j.inffus.2024.102608.
    2. R. Patil and R. Aggarwal, “COMPREHENSIVE REVIEW ON IMAGE SEGMENTATION APPLICATIONS,” Jan. 09, 2023, Social Science Research Network, Rochester, NY: 5227277. https://doi.org/10.2139/ssrn.5227277.
    3. Y. Yu et al., “Techniques and Challenges of Image Segmentation: A Review,” Electronics, vol. 12, no. 5, Art. no. 5, Jan. 2023, https://doi.org/10.3390/electronics12051199.
    4. Y. Chen et al., “Research of improving semantic image segmentation based on a feature fusion model,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 11, pp. 5033–5045, Nov. 2022, https://doi.org/10.1007/s12652-020-02066-z.
    5. H. Mittal, A. C. Pandey, M. Saraswat, S. Kumar, R. Pal, and G. Modwel, “A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets,” Multimed. Tools Appl., vol. 81, no. 24, pp. 35001–35026, Oct. 2022, https://doi.org/10.1007/s11042-021-10594-9.
    6. S. Shamas, S. N. Panda, and I. Sharma, “K-Means Clustering using Fuzzy C-Means Based Image Segmentation for Lung Cancer,” in 2022 3rd In-ternational Conference on Computation, Automation and Knowledge Management (ICCAKM), Nov. 2022, pp. 1–5. https://doi.org/10.1109/ICCAKM54721.2022.9990514.
    7. H. Zhang and J. Liu, “Fuzzy c-means clustering algorithm with deformable spatial information for image segmentation,” Multimed. Tools Appl., vol. 81, no. 8, pp. 11239–11258, Mar. 2022, https://doi.org/10.1007/s11042-022-11904-5.
    8. R. V. Patil, R. Aggarwal, G. M. Poddar, M. Bhowmik, and M. K. Patil, “Embedded Integration Strategy to Image Segmentation Using Canny Edge and K-Means Algorithm,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 13s, Art. no. 13s, Jan. 2024. https://doi.org/10.2139/ssrn.5095501.
    9. A. Naseir, “A comparison Study of Image Edge Segmentation Methods using Prewitt, Sobel and Laplacian of Gaussian for Medical Images,” J. Educ. Pure Sci., vol. 12, no. 2, Art. no. 2, Feb. 2023.
    10. S. Gupta, H. Singh, and Y. J. Singh, “Comprehensive Study on Edge Detection,” in Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technology, S. N. Singh, S. Mahanta, and Y. J. Singh, Eds., Singapore: Springer Nature, 2023, pp. 445–462. https://doi.org/10.1007/978-981-99-1699-3_30.
    11. Y. Lu, L. Duanmu, Z. (John) Zhai, and Z. Wang, “Application and improvement of Canny edge-detection algorithm for exterior wall hollowing detection using infrared thermal images,” Energy Build., vol. 274, p. 112421, Nov. 2022, https://doi.org/10.1016/j.enbuild.2022.112421.
    12. M. D. Ramasamy et al., “A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images,” Neural Comput. Appl., vol. 36, no. 7, pp. 3513–3524, Mar. 2024, https://doi.org/10.1007/s00521-023-09324-z.
    13. M. K. Islam, M. S. Ali, M. S. Miah, M. M. Rahman, M. S. Alam, and M. A. Hossain, “Brain tumor detection in MR image using superpixels, prin-cipal component analysis and template based K-means clustering algorithm,” Mach. Learn. Appl., vol. 5, p. 100044, Sep. 2021, https://doi.org/10.1016/j.mlwa.2021.100044.
    14. P. E. Jebarani, N. Umadevi, H. Dang, and M. Pomplun, “A Novel Hybrid K-Means and GMM Machine Learning Model for Breast Cancer Detec-tion,” IEEE Access, vol. 9, pp. 146153–146162, 2021, https://doi.org/10.1109/ACCESS.2021.3123425.
    15. J. Jing, S. Liu, G. Wang, W. Zhang, and C. Sun, “Recent advances on image edge detection: A comprehensive review,” Neurocomputing, vol. 503, pp. 259–271, Sep. 2022, https://doi.org/10.1016/j.neucom.2022.06.083.
    16. R. V. Patil and K. C. Jondhale, “Edge based technique to estimate number of clusters in k-means color image segmentation,” in 2010 3rd Interna-tional Conference on Computer Science and Information Technology, Jul. 2010, pp. 117–121. https://doi.org/10.1109/ICCSIT.2010.5563647.
    17. R. V. Patil, R. Sonawane, G. M. Poddar, S. S. Deore, R. Aggarwal, and V. Turai, “Automatic Marker Generation to Similarity Based Region Merg-ing Algorithm using Edge Information,” J. Electr. Syst., vol. 20, no. 3s, Art. no. 3s, Apr. 2024, https://doi.org/10.52783/jes.1845.
    18. Q. Pu, Z. Xi, S. Yin, Z. Zhao, and L. Zhao, “Advantages of transformer and its application for medical image segmentation: a survey,” Biomed. Eng. OnLine, vol. 23, no. 1, Feb. 2024, https://doi.org/10.1186/s12938-024-01212-4.
    19. B. Liu, W. Wang, Y. Wu, and X. Gao, “Attention Swin Transformer UNet for Landslide Segmentation in Remotely Sensed Images,” Remote Sens., vol. 16, no. 23, p. 4464, Nov. 2024, https://doi.org/10.3390/rs16234464.
    20. Md. N. Islam, Md. S. Azam, Md. S. Islam, M. H. Kanchan, A. H. M. S. Parvez, and Md. M. Islam, “An improved deep learning-based hybrid model with ensemble techniques for brain tumor detection from MRI image,” Inform. Med. Unlocked, vol. 47, p. 101483, 2024, https://doi.org/10.1016/j.imu.2024.101483.
    21. Md. E. Rayed, S. M. S. Islam, S. I. Niha, J. R. Jim, M. M. Kabir, and M. F. Mridha, “Deep learning for medical image segmentation: State-of-the-art advancements and challenges,” Inform. Med. Unlocked, vol. 47, p. 101504, 2024, https://doi.org/10.1016/j.imu.2024.101504.
    22. B. Zhang, L. Liu, M. H. Phan, Z. Tian, C. Shen, and Y. Liu, “SegViT v2: Exploring Efficient and Continual Semantic Segmentation with Plain Vi-sion Transformers,” Int. J. Comput. Vis., vol. 132, no. 4, pp. 1126–1147, Apr. 2024, https://doi.org/10.1007/s11263-023-01894-8.
    23. M. Al-Dabagh, “Automated tumor segmentation in MR brain image using fuzzy c-means clustering and seeded region methodology,” IAES Int. J. Artif. Intell. IJ-AI, vol. 10, pp. 284–290, Jun. 2021, https://doi.org/10.11591/ijai.v10.i2.pp284-290.
    24. R. V. Patil and R. Aggarwal, “Edge Information based Seed Placement Guidance to Single Seeded Region Growing Algorithm,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 12s, Art. no. 12s, Jan. 2024. https://doi.org/10.2139/ssrn.5095458.
    25. H. Kaur, D. Koundal, and V. Kadyan, “Image Fusion Techniques: A Survey,” Arch. Comput. Methods Eng., vol. 28, no. 7, pp. 4425–4447, Dec. 2021, https://doi.org/10.1007/s11831-021-09540-7.
    26. T. Rahman and Md. S. Islam, “Image Segmentation Based on Fuzzy C Means Clustering Algorithm and Morphological Reconstruction,” in 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Feb. 2021, pp. 259–263. https://doi.org/10.1109/ICICT4SD50815.2021.9396873.
    27. J. Malhotra and S. Jha, “Fuzzy c-means clustering based colour image segmentation for tool wear monitoring in micro-milling,” Precis. Eng., vol. 72, pp. 690–705, Nov. 2021, https://doi.org/10.1016/j.precisioneng.2021.07.013.
    28. S. Asgari Taghanaki, K. Abhishek, J. P. Cohen, J. Cohen-Adad, and G. Hamarneh, “Deep semantic segmentation of natural and medical images: a review,” Artif. Intell. Rev., vol. 54, no. 1, pp. 137–178, Jan. 2021, https://doi.org/10.1007/s10462-020-09854-1.
    29. S. Kakarwal, “A Novel Approach for Detection, Segmentation, and Classification of Brain Tumors in MRI Images Using Neural Network and Special C Means Fuzzy Clustering Techniques,” Adv. Nonlinear Var. Inequalities, vol. 27, no. 3, Art. no. 3, Aug. 2024, https://doi.org/10.52783/anvi.v27.1449.
    30. B. Saha Tchinda, D. Tchiotsop, M. Noubom, V. Louis-Dorr, and D. Wolf, “Retinal blood vessels segmentation using classical edge detection filters and the neural network,” Inform. Med. Unlocked, vol. 23, p. 100521, Jan. 2021, https://doi.org/10.1016/j.imu.2021.100521.
    31. P. S. and J. A. S. K., “Object Segmentation Based on the Integration of Adaptive K-means and GrabCut Algorithm,” in 2022 International Confer-ence on Wireless Communications Signal Processing and Networking (WiSPNET), Mar. 2022, pp. 213–216. https://doi.org/10.1109/WiSPNET54241.2022.9767099.
    32. D. Krasnov, D. Davis, K. Malott, Y. Chen, X. Shi, and A. Wong, “Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detec-tion,” Entropy, vol. 25, no. 7, p. 1021, Jul. 2023, https://doi.org/10.3390/e25071021.
    33. X. Liu, L. Song, S. Liu, and Y. Zhang, “A Review of Deep-Learning-Based Medical Image Segmentation Methods,” Sustainability, vol. 13, no. 3, Art. no. 3, Jan. 2021, https://doi.org/10.3390/su13031224.
    34. P. Ma, H. Yuan, Y. Chen, H. Chen, G. Weng, and Y. Liu, “A Laplace operator-based active contour model with improved image edge detection performance,” Digit. Signal Process., vol. 151, p. 104550, Aug. 2024, https://doi.org/10.1016/j.dsp.2024.104550.
    35. D. Tang, Y. Xu, and X. Liu, “Application of an Improved Laplacian-of-Gaussian Filter for Bearing Fault Signal Enhancement of Motors,” Ma-chines, vol. 12, no. 6, Art. no. 6, Jun. 2024, https://doi.org/10.3390/machines12060389.
    36. J. E. Alcaraz-Chavez, A. C. Téllez-Anguiano, J. C. Olivares-Rojas, and G. M. Chávez-Campos, “Laplacian of Gaussian for Fast Cell Detection and Segmentation in Cervical Cytology to Help in Cancer Diagnosis,” Cureus, vol. 17, no. 2, p. e78519, doi: 10.7759/cureus.78519.
    37. R. V. Patil, R. Aggarwal, and S. Shivaji Deore, “Edge Segmentation based on Illumination Invariant Feature Detector Phase Congruency,” in 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Jan. 2024, pp. 91–96. https://doi.org/10.1109/ICMCSI61536.2024.00020.

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  • How to Cite

    Patil, D. R. V. ., Bhadane, D. Y. ., Dandavate, D. A. ., Shende, D. P. ., Poddar, D. G. M. ., & Patil, S. R. . (2025). Unsupervised Fuzzy C-Means Segmentation with Adaptive Cluster Count Derived from Laplacian of Gaussian Edge Maps. International Journal of Basic and Applied Sciences, 14(3), 222-231. https://doi.org/10.14419/ac56hj76