ArticleBiomimetics (Basel, Switzerland)2023
Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution.
Article in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Truck Cargo Load Change Detection: Depth Map Normalization and 1D Projection Matching.Sensors (Basel, Switzerland) · 2026Article
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- An intelligent platform for monitoring human serum albumin in urine based on lateral flow immunochromatography.Mikrochimica acta · 2026Article
- Centroid growth selective clustering method for surface defect detection in silicon nitride ceramic bearing rollers.Scientific reports · 2025Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer.Biomimetics (Basel, Switzerland) · 2024Article
- Adaptive Bi-Operator Evolution for Multitasking Optimization Problems.Biomimetics (Basel, Switzerland) · 2024Article
- RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs.Scientific reports · 2024Article
- Comparison of Choroidal Vascular Index Measurements in Pachychoroid Pigment Epitheliopathy using Manual ImageJ-Based Niblack and Protocol-Standardized Otsu Thresholding.Journal of current ophthalmologyArticle
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Authors and funding
4 authors.
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Abstract
A quick and effective way of segmenting images is the Otsu threshold method. However, the complexity of time grows exponentially as the number of thresolds rises. The aim of this study is to address the issues with the standard threshold image segmentation method's low segmentation effect and high time complexity. The two mutations differential evolution based on adaptive control parameters is presented, and the twofold mutation approach and adaptive control parameter search mechanism are used. Superior double-mutation differential evolution views Otsu threshold picture segmentation as an optimization issue, uses the maximum interclass variance technique as the objective function, determines the ideal threshold, and then implements multi-threshold image segmentation. The experimental findings demonstrate the robustness of the enhanced double-mutation differential evolution with adaptive control parameters. Compared to other benchmark algorithms, our algorithm excels in both image segmentation accuracy and time complexity, offering superior performance.
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