Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
A thresholding method based on two-dimensional fractional differentiation
Image and Vision Computing
Metaheuristic Clustering
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Short Communication: Image segmentation using PSO and PCM with Mahalanobis distance
Expert Systems with Applications: An International Journal
Data clustering using harmony search algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Effective FCM noise clustering algorithms in medical images
Computers in Biology and Medicine
A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain
Computers and Industrial Engineering
Hi-index | 0.00 |
In this paper, we propose an improvement method for image segmentation using the fuzzy c-means clustering algorithm (FCM). This algorithm is widely experimented in the field of image segmentation with very successful results. In this work, we suggest further improving these results by acting at three different levels. The first is related to the fuzzy c-means algorithm itself by improving the initialization step using a metaheuristic optimization. The second level is concerned with the integration of the spatial gray-level information of the image in the clustering segmentation process and the use of Mahalanobis distance to reduce the influence of the geometrical shape of the different classes. The final level corresponds to refining the segmentation results by correcting the errors of clustering by reallocating the potentially misclassified pixels. The proposed method, named improved spatial fuzzy c-means IFCMS, was evaluated on several test images including both synthetic images and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb) database. This method is compared to the most used FCM-based algorithms of the literature. The results demonstrate the efficiency of the ideas presented.