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A fast technique for image segmentation based on two Meta-heuristic algorithms
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A fast technique for image segmentation based on two Meta-heuristic algorithms
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Journal Article
A fast technique for image segmentation based on two Meta-heuristic algorithms
2020
Overview
Image segmentation is a primary task in image processing which is widely used in object detection and recognition. Multilevel thresholding is one of the prominent technique in the field of image segmentation. However, the computational cost of multilevel thresholding increases exponentially as the number of threshold value increases, which leads to use of meta-heuristic optimization to find the optimal number of threshold. To overcome this problem, this paper investigates the ability of two nature-inspired algorithms namely: antlion optimisation (ALO) and multiverse optimization (MVO). ALO is a population-based method and mimics the hunting behaviour of antlions in nature. Whereas, MVO is based on the multiverse theory which depicts that there is over one universe exist. These two metaheuristic algorithms are used to find the optimal threshold values using Kapur’s entropy and Otsu’s between class variance function. They examine the outcomes of the proposed algorithm with other evolutionary algorithms based on cost value, stability analysis, feature similarity index (FSIM), structural similarity index (SSIM), peak signal to noise ratio (PSNR), computational time. We also provide Wilcoxon test which justify the response of these parameters. The experimental results showed that the proposed algorithm gives better results than other existing methods. It is noticed that MVO is faster than other algorithms. The proposed method is also tested on medical images to detect the tumor from MRI T1-weighted contrast-enhanced brain images.
Publisher
Springer Nature B.V
Subject
