Multilevel thresholding image segmentation based on improved volleyball premier league algorithm using whale optimization algorithm

Mohamed Abd Elaziz, Nabil Neggaz, Reza Moghdani, Ahmed A. Ewees, Erik Cuevas, Songfeng Lu

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.
Original languageEnglish
Pages (from-to)12435–12468
Number of pages34
JournalMultimedia Tools and Applications
Issue number8
Early online date11 Jan 2021
Publication statusPublished - 1 Mar 2021
Externally publishedYes

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