A multiverse optimization based colour image segmentation using variational mode decomposition

Abstract

Multilevel thresholding using the histogram is the most popular and accepted technique of image segmentation. The computational time of multilevel thresholding rises exponentially as the number of the thresholds increases. Histogram suffers from irregularities and sharp details which leads to stagnation. In this article, a newly developed multiverse optimization is combined with variational mode decomposition to overcome this problem. The histogram of an input image divided into several band-limit modes using VMD then reconstruct histogram using meaningful modes. The reconstructed histogram is free from the high-frequency fluctuation which causes local optima. The proposed method utilized two entropy function to develop image segmentation by determining the optimal threshold. The result of the proposed algorithm is analyzed with other evolutionary algorithms such as artificial bee colony, sine cosine algorithm, and salp swarm algorithm. Comparison is made based on comparative parameters such as peak signal to noise ratio, structural similarity index, feature similarity index, uniformity, normalized absolute error, quality index based on local variance, computational time, and mean square error. The test results validate that the proposed algorithm presents more reliable results than other existing techniques.