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Modified Grasshopper Algorithm-Based Multilevel Thresholding for Color Image Segmentation

Multilevel thresholding is an important approach for image segmentation which has drawn much attention during the past few years. The Tsallis entropy method is implemented for its effectiveness and simplicity. Although it is efficient and gives an excellent result in the case of bi-level thresholding, its evaluation becomes complexity when the number of thresholds increases. To overcome the problem, the metaheuristic algorithms are applied in this search area for searching the optimal thresholds. In this paper, a modified grasshopper optimization algorithm (GOA) is adopted to render multilevel Tsallis cross entropy more practical and reduce the complexity. The Levy flight algorithm is employed to modify the original GOA and balance the exploration and exploitation of the GOA. Experiments are conducted between five state-of-the-art metaheuristic algorithms and the proposed one. In addition, the proposed approach is compared with thresholding techniques depending on between-class variance (Otsu) method and the Renyi entropy function. Both real life images and plant stomata images are used in the experiments to test the performance of the algorithms involved. Qualitative experimental results show that the proposed segmentation approach has a fewer iterations and a higher segmentation accuracy.