Multi-threshold image segmentation based on an improved differential evolution: case study of thyroid papillary carcinoma

dc.creatorChen, Jiaochen
dc.creatorCai, Zhennao
dc.creatorHeidari, Ali Asghar
dc.creatorChen, Huiling
dc.creatorHe, Qiuxiang
dc.creatorEscorcia-Gutierrez, José
dc.creatorMansour, Romany F.
dc.date2023-05-19T22:52:06Z
dc.date2025
dc.date2023-05-19T22:52:06Z
dc.date2023
dc.date.accessioned2023-10-03T20:07:25Z
dc.date.available2023-10-03T20:07:25Z
dc.identifierJiaochen Chen, Zhennao Cai, Ali Asghar Heidari, Huiling Chen, Qiuxiang He, José Escorcia-Gutierrez, Romany F. Mansour, Multi-threshold image segmentation based on an improved differential evolution: Case study of thyroid papillary carcinoma, Biomedical Signal Processing and Control, Volume 85, 2023, 104893, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2023.104893
dc.identifier1746-8094
dc.identifierhttps://hdl.handle.net/11323/10155
dc.identifier10.1016/j.bspc.2023.104893
dc.identifier1746-8108
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174381
dc.descriptionThe scholarly world has demonstrated an immense enthusiasm for medical image segmentation due to its intricate nature and critical role in medical diagnosis and treatment systems. Multi-threshold image segmentation (MTIS) is a popular technique for this purpose, due to its simplicity and straightforwardness. This paper presents an improved Differential Evolution (DE) algorithm called AGDE, which is based on MTIS and was used to evaluate its high capability at IEEE CEC 2017. Comparisons with classical and advanced algorithms were conducted as part of the experiments. An AGDE-based multi-threshold image segmentation method utilizing a non-local mean 2D histogram in combination with Rényi's entropy was applied to segment images from the Berkeley Segmentation Datasets 500 (BSDS500) and microscopic images of thyroid papillary carcinoma (TPC). The experimental results showed that the proposed image segmentation method outperformed its competitors, making it a promising approach for medical image segmentation.
dc.format1 página
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier BV
dc.publisherNetherlands
dc.relationBiomedical Signal Processing and Control
dc.relation85
dc.rights© 2023 Elsevier Ltd. All rights reserved.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourcehttps://www.sciencedirect.com/science/article/abs/pii/S1746809423003269?via%3Dihub
dc.subjectMedical image segmentation
dc.subjectNon-local mean 2D histogram
dc.subject2D Rényi's entropy
dc.subjectDifferential evolution
dc.subjectDE algorithm
dc.subjectDE Image
dc.titleMulti-threshold image segmentation based on an improved differential evolution: case study of thyroid papillary carcinoma
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


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