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dc.contributor.authorVelázquez Blázquez, José Sebastián 
dc.contributor.authorCavas Martínez, Francisco 
dc.contributor.authorCampuzano Brando, Víctor Andrés 
dc.contributor.authorAlió del Barrio, Jorge Luis 
dc.contributor.authorFernández Cañavate, Francisco José 
dc.contributor.authorAlió Sanz, Jorge Luciano 
dc.date.accessioned2021-04-28T10:43:13Z
dc.date.available2021-04-28T10:43:13Z
dc.date.issued2020
dc.identifier.citationVELÁZQUEZ-BLÁZQUEZ, José Sebastián, CAVAS-MARTÍNEZ, Francisco, CAMPUZANO-BRANDO, Victor Andrés et al. AUTOMATIC IMAGE PROCESSING APPLIED TO CORNEAL ENDOTHELIUM CELL COUNT AND SHAPE CHARACTERIZATION. DYNA, March 2020, vol. 95, no. 2, p.170-174. DOI: https://doi.org/10.6036/9275es_ES
dc.identifier.issn0012-7361
dc.description.abstractCorneal endothelium cell count, as well as cell hexagonality percent characterization, are of great importance nowadays to detect anomalies and pathologies of human eye, such as glaucoma. Prevalent technologies used are mainly based in both microscopy and a later image analysis. However, automatic cell count made by microscopes’ built-in software is rather inconsistent, therefore many laboratories opt for using manual count as the most reliable alternative. This count is a tedious and time-consuming task, that can lead to human error, for this reason, several proposals to automate the process have been made. Present communication shows a procedure for the automatic pre-processing, segmentation and analysis of the images obtained by a confocal microscope, using watershed transform, and the graphics user interface (GUI) created with Matlab® to apply this procedure. In order to quantify the procedure’s quality, 30 corneal endothelium images with a number of cells between 90 and 170 were analysed, resulting in a mean error in cell count of 4.3%, which can be considered a reasonably good result. However, results achieved for hexagonality percent using this method, and with the available image quality, are not as good as expected, which invites to improving image quality, focusing in areas with better cell homogeneity or even considering the application of other algorithms, such as neural networks, for future works.es_ES
dc.description.sponsorshipThis work was supported by the Thematic Network for Co-Operative Research in Health (RETICS-RD16/0008/0012), financed by the Carlos III Health Institute and the European Regional Development Fund (FEDER).es_ES
dc.formatapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherFederación de Asociaciones de Ingenieros Industriales de Españaes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleAutomatic image processing applied to corneal endothelium cell count and shape characterizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subjectMatlab®es_ES
dc.subjectGraphics User Interface (GUI)es_ES
dc.subjectHexagonalityes_ES
dc.subjectWatershedes_ES
dc.subjectOpening-Closing by reconstruction (OCBR)es_ES
dc.subject.otherExpresión Gráfica en Ingenieríaes_ES
dc.identifier.urihttp://hdl.handle.net/10317/9328
dc.identifier.doi10.6036/9275
dc.identifier.urlhttps://www.revistadyna.com/search/automatic-image-processing-applied-to-corneal-endothelium-cell-count-and-shape-characterization
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.relation.projectIDRETICS-RD16/0008/0012es_ES
dc.subject.unesco3201.09 Oftalmologíaes_ES
dc.subject.unesco1203.09 Diseño Con Ayuda del Ordenadores_ES
dc.contributor.funderInstituto de Salud Carlos IIIes_ES
dc.contributor.funderFondo Europeo de Desarrollo Regional‏ (FEDER)es_ES


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