In parametric techniques, each group of grayscale range should be consistent with a Gaussian distribution. To obtain the best threshold values in MTH segmentation, thresholding techniques can be classified into two approaches: non-parametric and parametric. Bi-level thresholding techniques use one threshold to separate an image into two groups, whereas multi-level thresholding (MTH) uses two or more thresholds to separate an image into many groups 1. Image thresholding approaches can be categorized into two types: multi-level and bi-level thresholding. Thresholding obtains the information of the histogram from an image and determines the best threshold (( th)) for categorizing the pixels into various groups. To define the thresholds, most methods use the histogram of the image 5, which is vital for determining the probability distribution value of pixels in the image 6. Thresholding is one of the most common image segmentation approaches. It is utilized in various scopes, such as industry and medicine 2, agriculture 3, and surveillance 4. Segmentation is the process of separating an image into two or more homogeneous segments based on the characteristics of the pixels in the image. Segmentation has an important role in the field of image processing 1. Therefore, the IHBO algorithm was found to be superior to other segmentation methods for MTH image segmentation. The experimental results revealed that the proposed IHBO algorithm outperformed the counterparts in terms of the fitness values as well as other performance indicators, such as the structural similarity index (SSIM), feature similarity index (FSIM), peak signal-to-noise ratio. The performance of the IHBO-based method was evaluated on the CEC’2020 test suite and compared against seven well-known metaheuristic algorithms including the basic HBO, salp swarm algorithm, moth flame optimization, gray wolf optimization, sine cosine algorithm, harmony search optimization, and electromagnetism optimization. The IHBO was proposed to improve the convergence rate and local search efficiency of search agents of the basic HBO, the IHBO is applied to solve the problem of MTH using the Otsu and Kapur methods as objective functions. This paper integrates an efficient method for MTH image segmentation called the heap-based optimizer (HBO) with opposition-based learning termed improved heap-based optimizer (IHBO) to solve the problem of high computational cost for MTH and overcome the weaknesses of the original HBO. Methods such as the Kapur entropy or the Otsu method, which can be used as objective functions to determine the optimal threshold, are efficient in determining the best threshold for bi-level thresholding however, they are not effective for MTH due to their high computational cost. Multilevel thresholding (MTH) is a method used to perform this task, and the problem is to obtain an optimal threshold that properly segments each image. Image segmentation is the process of separating pixels of an image into multiple classes, enabling the analysis of objects in the image.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |