Unsupervised Fuzzy C-Means Segmentation with Adaptive Cluster Count Derived from Laplacian of Gaussian Edge Maps - Patil, Dr. Rajendra Vasantrao; Bhadane, Deepak Yashwantrao; Dandavate, Dr. Abhijit; Shende, Dr Priti; Poddar, Dr. Govind Mohanlal; Patil, Shravani R. (International Journal of Basic and Applied Sciences, 2025)
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.
Keywords: Fuzzy C-Means; Image Segmentation; Adaptive Clustering; Laplacian of Gaussian; Edge Detection; Embedded Integration; Unsupervised ‎Learning.