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A novel neuro-fuzzy-genetic approach is presented in this article to segment a true color image into different color levels. A MUSIG activation function induces multiscaling capabilities in a parallel self organizing neural network (PSONN) architecture. The function however resorts to equal and fixed class responses, assuming the homogeneity of image(More)
Automatic data clustering through determination of optimal number of clusters from the data content, is a challenging proposition. Lack of knowledge regarding the underlying data distribution poses constraints in proper determination of the inherent number of clusters.
Based on different criteria any real life problem generates a set of alternative solutions instead of a single optimal solution. Color image segmentation by single objective based parallel optimized MUSIG (ParaOptiMUSIG) activation function may or may not render better solutions for different objective functions. To overcome this problem, a non-dominated(More)
Medical image segmentation is a challenging task for analyzing the magnetic resonance (MRI) images. These type of images contain missing or diffuse organ/tissue boundaries due to poor image contrast. Medical image segmentation can be addressed effectively by genetic algorithms (GAs). In this article, an application of pixel intensity based medical image(More)
Convergence to local minima point is one of the major disadvantages of conventional fuzzy c-means (FCM). Due to this drawback, segmentation result may hamper for not selecting the cluster centroids properly. To overcome this, a modified genetic (MfGA) algorithm is proposed to improve the performance of FCM. The optimized class levels derived from the MfGA(More)
One must have a prior knowledge about the optimal number of clusters in a data set before clustering. Without having information regarding the exact nature of the underlying data distribution, the determination of optimal number of clusters in an unlabeled data set is not an easy task. Genetic algorithms (GAs) is known as a randomized search and(More)