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dc.contributor.authorRivas Villar, David
dc.contributor.authorRouco Maseda, José
dc.contributor.authorGonzález Penedo, Manuel
dc.contributor.authorCarballeira, Rafael
dc.contributor.authorNovo Buján, Jorge
dc.date.accessioned2022-03-04T07:47:08Z
dc.date.available2022-03-04T07:47:08Z
dc.date.issued2020
dc.identifier.issn1424-8220
dc.identifier.otherhttps://www.ncbi.nlm.nih.gov/pubmed/33238566es
dc.identifier.urihttp://hdl.handle.net/20.500.11940/16173
dc.description.abstractWater safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.es
dc.language.isoenes
dc.rightsAtribución 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.meshFresh Water*
dc.subject.meshEnvironmental Monitoring*
dc.subject.meshAlgorithms*
dc.titleAutomatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Imagesen
dc.typeJournal Articlees
dc.authorsophosRivas-Villar, David;Rouco, José;Penedo, Manuel G;Carballeira, Rafael;Novo, Jorge
dc.identifier.doi10.3390/s20226704
dc.identifier.pmid33238566
dc.identifier.sophos35681
dc.issue.number22es
dc.journal.titleSENSORSes
dc.organizationServizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::Instituto de Investigación Biomédica da Coruña (INIBIC)es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/20/22/6704es
dc.rights.accessRightsopenAccess
dc.subject.decsagua dulce *
dc.subject.decsalgoritmos *
dc.subject.decsmonitorización del ambiente *
dc.subject.keywordINIBICes
dc.typefidesArtículo Originales
dc.typesophosArtículo Originales
dc.volume.number20es


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Atribución 4.0 Internacional
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