View in EDS

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

Saved in:
Bibliographic Details
Title: A multi-threshold image segmentation method based on arithmetic optimization algorithm: A real case with skin cancer dermoscopic images
Authors: Shuhui Hao, Changcheng Huang, Yi Chen, Mingjing Wang, Lei Liu, Suling Xu, Huiling Chen
Source: Journal of Computational Design and Engineering. 12:112-137
Publisher Information: Oxford University Press (OUP), 2025.
Publication Year: 2025
Description: 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.
Document Type: Article
Language: English
ISSN: 2288-5048
DOI: 10.1093/jcde/qwaf006
Rights: CC BY
Accession Number: edsair.doi...........d65fbdcc3b7d649ff0d24ce6cadc772e
Database: OpenAIRE
Description
Abstract: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.
ISSN:22885048
DOI:10.1093/jcde/qwaf006