International Journal of Nonlinear Analysis and Applications
An improved grasshopper optimization algorithm for multilevel thresholding image segmentation
Document Type : Research Paper
Authors
1 Deartment of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran
2 Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran
3 Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran
Abstract
Multilevel thresholding is one of the most common, straightforward, and effective image segmentation algorithms. The most important issue in this method is choosing an appropriate threshold value. In such a way that by defining worthy thresholds, the image can be more accurately segmented. The Otsu approach is suitable for establishing the thresholds at two levels, but as the number of thresholds increases, the performance of Otsu diminishes in terms of time and segmentation accuracy. On the other hand, optimization techniques can be effective to address these challenges. As a result, it is used with optimization techniques to improve time and segmentation accuracy. In this paper, we propose an improved grasshopper optimization approach to enhance the quality of the segmented image and its accuracy. In the proposed method, multilevel thresholding image segmentation is performed by employing the Otsu method as an objective function. This research aims to enhance the grasshopper algorithm to improve image segmentation outcomes. For this purpose, various modifications were applied to the grasshopper method. The proposed algorithm is evaluated on some known images and compared with several optimization algorithms. The resultant modified grasshopper method outperforms other evolutionary algorithms like Whale, Firefly, and Artificial Bee Colony (ABC) optimization algorithms. The proposed IGOA algorithm outperforms other approaches at PSNR metric for threshold levels of 32 and 64 for 87.5% and 100% of images, respectively. Additionally, at SSIM metric, for both threshold levels of 32 and 64, it overcomes other approaches for 100% of images.
Keywords
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Articles in Press, Corrected Proof
Available Online from 26 April 2026
- Receive Date: 19 March 2024
- Revise Date: 01 December 2024
- Accept Date: 18 November 2025