A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Digital Image Processing
An Extensible MRI Simulator for Post-Processing Evaluation
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters
IEEE Transactions on Knowledge and Data Engineering
Scatter Search for the Point-Matching Problem in 3D Image Registration
INFORMS Journal on Computing
MR Brain Image Segmentation Using A Multi-seed Based Automatic Clustering Technique
Fundamenta Informaticae
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
Deflation-based power iteration clustering
Applied Intelligence
MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI
Computer Methods and Programs in Biomedicine
Skin cancer extraction with optimum fuzzy thresholding technique
Applied Intelligence
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In this paper, the automatic segmentation of a multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. The automatic clustering problem is thereafter modelled as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. A multiobjective clustering technique, named MCMOClust, is used to solve this problem. MCMOClust utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously to automatically evolve the appropriate number of clusters present in MR brain images. A semi-supervised method is used to select a single solution from the final Pareto optimal front of MCMOClust. The present method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the present method over Fuzzy C-means, Expectation Maximization clustering algorithms and a newly developed symmetry based fuzzy genetic clustering technique (Fuzzy-VGAPS), are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCMOClust) is also compared with the available ground truth information.