Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer

Mohamed Abd Elaziz, Diego Oliva, Ahmed A. Ewees, Shengwu Xiong

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


Image segmentation is among the most important techniques in image processing, and many methods have been developed to perform this task. This paper presents a new multi-objective metaheuristic based on a multi-verse optimization algorithm to segment grayscale images via multi-level thresholding. The proposed approach involves finding an approximate Pareto-optimal set by maximizing the Kapur and Otsu objective functions. Both Kapur's and Otsu's methods are highly used for image segmentation performed by means of bi-level and multi-level thresholding. However, each of them has certain characteristics and limitations. Several metaheuristic approaches have been proposed in the literature to separately optimize these objective functions in terms of accuracy, whereas only a few multi-objective approaches have explored the benefits of the joint use of Kapur and Otsu's methods. However, the computational cost of Kapur and Otsu is high and their accuracy needs to be improved. The proposed method, called Multi-objective Multi-verse Optimization, avoids these limitations. It was tested using 11 natural grayscale images and its performance was compared against three of well-known multi-objective algorithms. The results were analyzed based on two sets of measures, one to assess the performance of the proposed method as a multi-objective algorithm, and the other to evaluate the accuracy of the segmented images. The results showed that the proposed method provides a better approximation to the optimal Pareto Front than the other algorithms in terms of hypervolume and spacing. Moreover, the quality of its segmented image is better than those of the other methods in terms of uniformity measures.

Original languageEnglish
Pages (from-to)112-129
Number of pages18
JournalExpert Systems with Applications
Publication statusPublished - 1 Jul 2019
Externally publishedYes


  • Image segmentation
  • Multi-level thresholding
  • Multi-objective optimization
  • Multi-verse optimizer

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer'. Together they form a unique fingerprint.

Cite this