Multilevel thresholding using grey wolf optimizer for image segmentation

Citation data:

Expert Systems with Applications, ISSN: 0957-4174, Vol: 86, Page: 64-76

Publication Year:
2017
Usage 58
Abstract Views 58
Captures 26
Readers 26
Social Media 228
Shares, Likes & Comments 228
Citations 8
Citation Indexes 8
DOI:
10.1016/j.eswa.2017.04.029
Author(s):
Abdul Kayom Md Khairuzzaman; Saurabh Chaudhury
Publisher(s):
Elsevier BV
Tags:
Engineering; Computer Science
article description
Multilevel thresholding is one of the most important areas in the field of image segmentation. However, the computational complexity of multilevel thresholding increases exponentially with the increasing number of thresholds. To overcome this drawback, a new approach of multilevel thresholding based on Grey Wolf Optimizer (GWO) is proposed in this paper. GWO is inspired from the social and hunting behaviour of the grey wolves. This metaheuristic algorithm is applied to multilevel thresholding problem using Kapur's entropy and Otsu's between class variance functions. The proposed method is tested on a set of standard test images. The performances of the proposed method are then compared with improved versions of PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based multilevel thresholding methods. The quality of the segmented images is computed using Mean Structural SIMilarity (MSSIM) index. Experimental results suggest that the proposed method is more stable and yields solutions of higher quality than PSO and BFO based methods. Moreover, the proposed method is found to be faster than BFO but slower than the PSO based method.