Using the buzzard optimization algorithm for multilevel thresholding of brain CT images

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Electrical Engineering, Isfahan Branch (Khorasgan), Islamic Azad University, Isfahan, Iran

Abstract

The process of image segmentation involves dividing a digital image into multiple parts to simplify or change its representation into something more meaningful and easier to analyze. Although modern deep learning-based methods have emerged, thresholding methods remain widely used due to their significantly lower complexity. In this paper, a new multilevel thresholding algorithm for histogram-based segmentation of CT images is presented. In the proposed algorithm, image thresholding is performed using the recently introduced buzzard optimization (BUZO) algorithm. In BUZO, the process of exploration and exploitation is achieved by defining several types of buzzards with different capabilities. Entropy, as the fitness function of the BUZO algorithm, is used to perform multilevel image segmentation. The proposed algorithm is compared with two evolutionary optimization algorithms: particle swarm optimization (PSO) and an improved version of PSO based on the fuzzy multi-agent system. The comparison on a set of CT images shows an average 8% superiority of the proposed method in fitness functions. Moreover, the quality of segmented images shows approximately 3% and 5% improvement in two- and five-level segmented images, respectively.

Keywords

References

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History

  • Receive Date: 19 December 2021
  • Revise Date: 19 July 2022
  • Accept Date: 13 August 2022

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