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dc.contributor.authorKubíček, Jan
dc.contributor.authorPenhaker, Marek
dc.contributor.authorAugustynek, Martin
dc.contributor.authorČerný, Martin
dc.contributor.authorOczka, David
dc.date.accessioned2019-10-14T08:50:20Z
dc.date.available2019-10-14T08:50:20Z
dc.date.issued2019
dc.identifier.citationSymmetry. 2019, vol. 11, issue 7, art. no. 861.cs
dc.identifier.issn2073-8994
dc.identifier.urihttp://hdl.handle.net/10084/138836
dc.description.abstractArticular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesSymmetrycs
dc.relation.urihttp://doi.org/10.3390/sym11070861cs
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectarticular cartilagecs
dc.subjectosteoarthritiscs
dc.subjectevolutionary optimizationcs
dc.subjectregional segmentationcs
dc.subjectMRIcs
dc.titleSegmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregationcs
dc.typearticlecs
dc.identifier.doi10.3390/sym11070861
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume11cs
dc.description.issue7cs
dc.description.firstpageart. no. 861cs
dc.identifier.wos000481979000026


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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.