ETACM: an encoded-texture active contour model for image segmentation with fuzzy boundaries

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dc.authoridJafarzadeh-Ghoushchi, Saeid/0000-0003-3665-9010
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authorwosidJafarzadeh-Ghoushchi, Saeid/AAC-7253-2019
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorRanjbarzadeh, Ramin
dc.contributor.authorSadeghi, Soroush
dc.contributor.authorFadaeian, Aida
dc.contributor.authorGhoushchi, Saeid Jafarzadeh
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorCaputo, Annalina
dc.contributor.authorBendechache, Malika
dc.date.accessioned2024-05-19T14:38:48Z
dc.date.available2024-05-19T14:38:48Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractActive contour models (ACMs) have been widely used in image segmentation to segment objects. However, when it comes to segmenting images with severe intensity inhomogeneity, most current frameworks do not perform well, which can make it difficult to achieve the desired results. To address this issue, a decision-making model is proposed, which involves using enhanced local direction pattern (ELDP) and local directional number pattern (LDNP) texture descriptors to create an encoded-texture ACM. The principal component analysis (PCA) is then used to optimize the two encoded images and reduce the correlations before they are fused. To further improve the performance of the encoded-texture ACM, a function of minimizing energy globally (FMEG) is suggested by applying the vector-valued exploration technique from a non-convex surface to region-based ACMs. This approach enables the development of a model capable of directly building complex decision boundaries. The experimental results show that the proposed encoded-texture ACM outperforms many recent frameworks in terms of robustness and accuracy for segmenting images with intensity inhomogeneity, fuzzy boundaries, and noise. Therefore, the suggested approach provides a more effective and efficient solution to the problem of image segmentation, particularly for challenging images.en_US
dc.description.sponsorshipScience Foundation Ireland [18/CRT/6183]; ADAPT Centre for Digital Content Technology under the SFI Research Centres Programme [13/RC/2106/_P2]; Lero SFI Centre for Software [13/RC/2094/_P2]; European Regional Development Funden_US
dc.description.sponsorshipThis publication has emanated from research conducted with the financial support of/supported in part by a grant from Science Foundation Ireland under Grant number No. 18/CRT/6183 and is supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106/_P2), Lero SFI Centre for Software (Grant 13/RC/2094/_P2) and is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.en_US
dc.identifier.doi10.1007/s00500-023-08983-3
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.scopus2-s2.0-85165925417en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1007/s00500-023-08983-3
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4614
dc.identifier.wosWOS:001034632900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectActive Contour Modelen_US
dc.subjectFeature Fusionen_US
dc.subjectFuzzy Boundaryen_US
dc.subjectDecision Modelen_US
dc.subjectSegmentationen_US
dc.titleETACM: an encoded-texture active contour model for image segmentation with fuzzy boundariesen_US
dc.typeArticleen_US

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