Efficient grey-level image segmentation using an optimised MUSIG (OptiMUSIG) activation function

Abstract

The conventional multilevel sigmoidal (MUSIG) activation function is efficient in segmenting multilevel images. The function uses equal and fixed class responses, thereby ignoring the heterogeneity of image information content. In this article, a novel approach for generating optimised class responses of the MUSIG activation function is proposed so that image content heterogeneity can be incorporated in the segmentation procedure. Four different types of objective function are used to measure the quality of the segmented images in the proposed genetic algorithm-based optimisation method. Results of segmentation of one synthetic and two real-life images by the proposed optimised MUSIG (OptiMUSIG) activation function with optimised class responses show better performances over the conventional MUSIG counterpart with equal and fixed responses. Comparative studies with the standard fuzzy c-means (FCM) algorithm, efficient in clustering of multidimensional data, also reveal better performances of the proposed function.

Keywords:

• multilevel image segmentation
• MUSIG activation function
• multilayer self-organising neural network
• optimisation procedures
• fuzzy c-means
• segmentation efficiency