Multi-strategy ant colony optimization for multi-level image segmentation: Case study of melanoma

Zhao, Dong; Qi, Ailiang; Fu, Yu; Heidari, Ali Asghar; Chen, Huiling; Li, Yangyang

doi:10.1016/j.bspc.2023.104647

Cited by 17 publications

(4 citation statements)

References 103 publications

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“…Boundary smoothing improves the overall quality and interpretability of segmented pictures by improving the segmentation result using post-processing techniques. In this study, they look at image segmentation technology based on the Ant Colony Algorithm, with a particular emphasis on the use of border smoothing techniques [6]. They examine the core ideas of ACO and its application to image segmentation tasks, emphasizing its strengths and limits [7].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Technology Based on Ant Colony Algorithm

Xiaoyan Wang

2024

jes

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Image segmentation is a key task in computer vision, with applications ranging from medical diagnosis to autonomous driving. The Ant Colony Algorithm (ACO), modeled after ant foraging behavior, has emerged as a viable segmentation methodology. However, ACO-based segmentation algorithms frequently generate segmented outputs with jagged or uneven boundaries, which reduces their interpretability and usability. To alleviate this problem, they study the use of boundary-smoothing approaches in ACO-based segmentation. In this paper, they investigate image segmentation technology based on the Ant Colony Algorithm, with a focus on border smoothing. They examine the fundamentals of ACO and its application to image segmentation, emphasizing its strengths and limits. They also look at several boundary smoothing strategies, such as morphological operations, edge-preserving filters, and active contours (snakes), and how they affect segmentation performance. Through experimental validation and comparative analysis, they show that boundary smoothing improves the accuracy and visual quality of segmented images produced by ACO-based segmentation algorithms. These results help to design more robust and visually appealing segmentation algorithms, which have potential applications in medical imaging, remote sensing, and industrial automation.

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Section: Introductionmentioning

confidence: 99%

Image Segmentation Technology Based on Ant Colony Algorithm

Xiaoyan Wang

2024

jes

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“…Improving the convergence speed and convergence performance of the algorithm by introducing a specific diversity strategy and a specific convergence strategy. [25] 3. Using an adaptive weighting mechanism to generate different search agents according to their [26] 4.…”

Section: Introductionmentioning

confidence: 99%

A Novel Meta-Heuristic Algorithm for Numerical and Engineering Optimization Problems: Piranha Foraging Optimization Algorithm (PFOA)

et al. 2023

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This paper provides a novel meta-heuristic optimization algorithm for solving continuous optimization problems efficiently in the field of numerical and engineering optimization: Piranha Foraging Optimization Algorithm (PFOA). The algorithm is inspired by the flexible and mobile foraging behaviour of piranha swarm and divides their foraging behavior into three patterns: localized group attack, bloodthirsty cluster attack and scavenging foraging, simulates the above behaviors to construct two dynamic search processes for exploration and exploitation. PFOA uses three strategies of non-linear parameter control, population survival and reverse evasion search to enable populations to have better population diversity at different stages of the search and to help find better solutions. To gain insight into the performance of PFOA, visualization methods were used to assess the efficiency of PFOA optimization and to analyse the impact of the characteristics of the three foraging modes, the sensitivity of the parameters and the size of the piranha population on the algorithm. The algorithm performance was further tested with 27 CEC benchmark functions and four real engineering design optimization problems, and the results were compared with 13 well-known meta-heuristics. Test results based on statistical methods such as box-line plots, Wilcoxon rank sum test and Friedman test in multiple dimensions (30, 50, 100 and fixed dimensions) show significant differences compared to other algorithms and that the performance of the algorithm is stable and in significant improvement. The unique advantages of PFOA in terms of the equilibrium of convergence speed and exploration can avoid getting trapped in local optimum regions and effectively solve optimization problems in complex search spaces.

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“…These heuristics and improved algorithms have demonstrated significant potential in many application scenarios, such as engineering design problems, 61 , 62 , 63 image segmentation, 64 , 65 , 66 , 67 , 68 scheduling problems, 69 feature selection, 70 , 71 , 72 and financial stress prediction. 21 , 73 Many practices indicate that the enhanced approach performs better than the original algorithm in some optimization domains.…”

Section: Introductionmentioning

confidence: 99%

An accelerated sine mapping whale optimizer for feature selection

Yu,

Zhao,

Heidari

et al. 2023

iScience

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Multi-strategy ant colony optimization for multi-level image segmentation: Case study of melanoma

Cited by 17 publications

References 103 publications

Image Segmentation Technology Based on Ant Colony Algorithm

Image Segmentation Technology Based on Ant Colony Algorithm

A Novel Meta-Heuristic Algorithm for Numerical and Engineering Optimization Problems: Piranha Foraging Optimization Algorithm (PFOA)

An accelerated sine mapping whale optimizer for feature selection

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