Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation

Authors:
Mohamad Forouzanfar;Nosratallah Forghani;Mohammad Teshnehlab
Affiliations:
School of Information Technology and Engineering (SITE), University of Ottawa, 800 King Edward Avenue, Ottawa, Ontario, Canada K1N 6N5 and Department of Biomedical Engineering, Faculty of Electric ...;Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran, Iran;Department of Control Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran, Iran
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 11
Cited 4

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization and detection of noise in clustering

Pattern Recognition Letters
New ideas in optimization

New ideas in optimization
Spatial models for fuzzy clustering

Computer Vision and Image Understanding
Comparison between Genetic Algorithms and Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Breeding swarms: a GA/PSO hybrid

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

IEEE Transactions on Information Technology in Biomedicine
Optimality test for generalized FCM and its application to parameter selection

IEEE Transactions on Fuzzy Systems
A Generalized Fuzzy Clustering Regularization Model With Optimality Tests and Model Complexity Analysis

IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering

IEEE Transactions on Fuzzy Systems

Multi-objective fuzzy clustering method for image segmentation based on variable-length intelligent optimization algorithm

ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends

Machine Graphics & Vision International Journal
Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

Computer Methods and Programs in Biomedicine
An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A traditional approach to segmentation of magnetic resonance (MR) images is the fuzzy c-means (FCM) clustering algorithm. The efficacy of FCM algorithm considerably reduces in the case of noisy data. In order to improve the performance of FCM algorithm, researchers have introduced a neighborhood attraction, which is dependent on the relative location and features of neighboring pixels. However, determination of degree of attraction is a challenging task which can considerably affect the segmentation results. This paper presents a study investigating the potential of genetic algorithms (GAs) and particle swarm optimization (PSO) to determine the optimum value of degree of attraction. The GAs are best at reaching a near optimal solution but have trouble finding an exact solution, while PSO's-group interactions enhances the search for an optimal solution. Therefore, significant improvements are expected using a hybrid method combining the strengths of PSO with GAs, simultaneously. In this context, a hybrid GAs/PSO (breeding swarms) method is employed for determination of optimum degree of attraction. The quantitative and qualitative comparisons performed on simulated and real brain MR images with different noise levels demonstrate unprecedented improvements in segmentation results compared to other FCM-based methods.