Gray Scale Image Segmentation by NSGA-II Based OptiMUSIG Activation Function

A multilevel gray scale image can quite efficiently be segmented by the multilevel sigmoidal (MUSIG) activation function based on equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function can be generated with the optimized class responses from the image content and can be used effectively to segment the multilevel gray scale images. These methods may or may not generate a good quality segmented image as the segmentation criteria of these methods are based on single segmentation evaluation criterion. This article proposed a self supervised image segmentation method by a non-dominated sorting genetic algorithm-II (NSGA-II) based optimized MUSIG (OptiMUSIG) activation function with a multi layer self organizing neural network (MLSONN) architecture to segment a multilevel gray scale intensity images. Some standard objective functions are applied in this proposed method to measure the quality of the segmented images. These functions form the multiple objective criteria of the NSGA-II based image segmentation method.