Advances in evolutionary computing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A multiobjective approach to MR brain image segmentation
Applied Soft Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
AbYSS: Adapting Scatter Search to Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
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The aim of this paper is to propose and apply state-of-the-art multiobjective scatter search for solving image segmentation problem. The algorithm incorporates the concepts of Pareto dominance, external archiving, diversification and intensification of solutions. The multiobjective optimization method is Archive-based Hybrid Scatter Search (AbYSS) for image segmentation. It utilized fuzzy clustering method with optimization of two fitness functions, viz., the global fuzzy compactness of the clusters and the fuzzy separation. We have tested the methods on two types of grey scale images, namely SAR (synthetic aperture radar) image and CT scan (Computer Tomography) image. We then compared it with fuzzy c-means (FCM) and a popular evolutionary multiobjective evolutionary clustering named NSGA-II. The performance result for the proposed method is compatible with the existing methods.