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A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images

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Titel: A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images
Autoren: Shuhui Hao, Changcheng Huang, Yi Chen, Mingjing Wang, Lei Liu, Suling Xu, Huiling Chen
Quelle: Journal of Computational Design and Engineering. 12:112-137
Verlagsinformationen: Oxford University Press (OUP), 2025.
Publikationsjahr: 2025
Beschreibung: Multi-threshold image segmentation (MTIS) is a crucial technology in image processing, characterized by simplicity and efficiency, and the key lies in the selection of thresholds. However, the method's time complexity will grow exponentially with the number of thresholds. To solve this problem, an improved arithmetic optimization algorithm (ETAOA) is proposed in this paper, an optimizer for optimizing the process of merging appropriate thresholds. Specifically, two optimization strategies are introduced to optimize the optimal threshold process: elite evolutionary strategy (EES) and elite tracking strategy (ETS). First, to verify the optimization performance of ETAOA, mechanism comparison experiments, scalability tests, and comparison experiments with nine state-of-the-art peers are executed based on the benchmark functions of CEC2014 and CEC2022. After that, to demonstrate the feasibility of ETAOA in the segmentation domain, comparison experiments were performed using 10 advanced segmentation methods based on skin cancer dermatoscopy image datasets under low and high thresholds, respectively. The above experimental results show that the proposed ETAOA performs outstanding optimization compared with benchmark functions. Moreover, the experimental results in the segmentation domain show that ETAOA has superior segmentation performance under low and high threshold conditions.
Publikationsart: Article
Sprache: English
ISSN: 2288-5048
DOI: 10.1093/jcde/qwaf006
Rights: CC BY
Dokumentencode: edsair.doi...........d65fbdcc3b7d649ff0d24ce6cadc772e
Datenbank: OpenAIRE
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  Data: A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images
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  Data: <searchLink fieldCode="AR" term="%22Shuhui+Hao%22">Shuhui Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Changcheng+Huang%22">Changcheng Huang</searchLink><br /><searchLink fieldCode="AR" term="%22Yi+Chen%22">Yi Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Mingjing+Wang%22">Mingjing Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Lei+Liu%22">Lei Liu</searchLink><br /><searchLink fieldCode="AR" term="%22Suling+Xu%22">Suling Xu</searchLink><br /><searchLink fieldCode="AR" term="%22Huiling+Chen%22">Huiling Chen</searchLink>
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  Data: Oxford University Press (OUP), 2025.
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  Data: Multi-threshold image segmentation (MTIS) is a crucial technology in image processing, characterized by simplicity and efficiency, and the key lies in the selection of thresholds. However, the method's time complexity will grow exponentially with the number of thresholds. To solve this problem, an improved arithmetic optimization algorithm (ETAOA) is proposed in this paper, an optimizer for optimizing the process of merging appropriate thresholds. Specifically, two optimization strategies are introduced to optimize the optimal threshold process: elite evolutionary strategy (EES) and elite tracking strategy (ETS). First, to verify the optimization performance of ETAOA, mechanism comparison experiments, scalability tests, and comparison experiments with nine state-of-the-art peers are executed based on the benchmark functions of CEC2014 and CEC2022. After that, to demonstrate the feasibility of ETAOA in the segmentation domain, comparison experiments were performed using 10 advanced segmentation methods based on skin cancer dermatoscopy image datasets under low and high thresholds, respectively. The above experimental results show that the proposed ETAOA performs outstanding optimization compared with benchmark functions. Moreover, the experimental results in the segmentation domain show that ETAOA has superior segmentation performance under low and high threshold conditions.
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              Type: published
              Y: 2025
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