A Multilevel Thresholding Method Based on Multiobjective Optimization for Non-Supervised Image Segmentation

Abstract

The aim of this work is to provide a comprehensive review of multiobjective optimization in the image segmentation problem based on image thresholding. The authors show that the inclusion of several criteria in the thresholding segmentation process helps to overcome the weaknesses of these criteria when used separately. In this context, they give a recent literature review, and present a new multi-level image thresholding technique, called Automatic Threshold, based on Multiobjective Optimization (ATMO). That combines the flexibility of multiobjective fitness functions with the power of a Binary Particle Swarm Optimization algorithm (BPSO), for searching the “optimum” number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare with this segmentation method, based on the multiobjective optimization approach with Otsu’s, Kapur’s, and Kittler’s methods. Experimental results show that the thresholding method based on multiobjective optimization is more efficient than the classical Otsu’s, Kapur’s, and Kittler’s methods.

Thresholding of biological images by using evolutionary algorithms

“…A high value of PSNR means that the image has a better quality of segmentation. The equations used to compute the PSNR are given in [15] and the results are depicted in Table IV. In these results, we omit the value of PSNR for the Intra-class Otsu's method, because it consumes more computational effort according Table III.…”

“…The use of multiobjective optimization, evolutionary and bioinspired algorithm approaches have shown satisfactory results in the image thresholding [14], [15]. Otherwise, any thresholding criteria found in the literature for multiobjective thresholding are inter-class variance, the entropy, overall probability of error and Gaussian curve fitting method [16]- [19].…”

Tsallis Entropy In Bi-level And Multi-level Image Thresholding

“…This is a processing task which aims separating the image pixels in some manner, for instance, in pixels pertaining to objects or to the background. This very important process is the first step to understand the components of the image and for a recognition and extraction of their features [3].…”

“…Bi-level thresholding separates the pixels into two classes, one containing pixels with gray-levels below the threshold, the other with graylevels above it. Multi-level thresholding generalizes this to several classes [3]. Here, we will discuss the use of Tsallis entropy in bi-and multi-level image thresholding.…”