Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images

Abstract

The objective of multilevel thresholding is to segmenting a gray-level image into several distinct homogeneous regions. This paper presents an alternative approach for unsupervised segmentation of natural and medical images to improve the separation between objects in the framework of multi-objective optimization. In contrast to the existing single-objective optimization and entropy-based methods, a multi-objective framework is adopted by combining two objectives based on the Minimum Cross Entropy (MCE) and Rényi Entropy (RE). One of the most competitive Multi-Objective Evolutionary Algorithms (MOEAs) of current interest, called MOEA/D-DE (Decomposition based MOEA with Differential Evolution) is then applied to determine the set of Pareto optimal solutions for these two objectives. The threshold values for multi-level segmentations are obtained from the approximated Pareto Fronts (PFs) generated by MOEA/D-DE. The performance of MOEA/D-DE is also investigated extensively through comparison with other popular nature-inspired single-objective and multi-objective optimizers. Moreover, outcome of the proposed method is evaluated by comparing against the results of other well cited algorithms both qualitatively and quantitatively on test-suites comprising well-known natural and medical test images in order to showcase the efficiency of the proposed algorithm.