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Diseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificial

dc.contributor.advisorGonzález Acevedo, Hernandospa
dc.contributor.authorAza Mantilla, Jessica Paolaspa
dc.date.accessioned2020-06-26T19:45:26Z
dc.date.available2020-06-26T19:45:26Z
dc.date.issued2018-08-13
dc.identifier.urihttp://hdl.handle.net/20.500.12749/1614
dc.description.abstractLa retinopatía diabética es una de las afecciones más comunes de la diabetes [1], siendo la tercera causa de ceguera irreversible en el mundo, pero la primera en personas de edad productiva (16 a 64 años) que padecen de diabetes tipo 1 o tipo 2 por más de 10 años, los cuales cuentan con el 50% a 60% de probabilidad de desarrollarla. Para identificar la condición médica bajo una observación física del órgano ocular se realiza un examen con lámpara de hendidura o una cámara de fondo de ojo con pupilas dilatadas [2]. Dichas formas de diagnóstico consumen tiempo y con frecuencia requieren una angiografía con fluoresceína o una tomografía de coherencia óptica para confirmar el grado de la retinopatía diabética.spa
dc.description.tableofcontentsINTRODUCCIÓN ................................................................................................... 12 1. OBJETIVOS ....................................................................................................... 13 1.1 Objetivo General ........................................................................................... 13 1.2 Objetivos Específicos ................................................................................... 13 2. ANTECEDENTES .............................................................................................. 14 2.1 Retinopatía Diabética ................................................................................... 14 2.2 Fondo Ocular ................................................................................................ 15 2.3 Base de datos de retina ................................................................................ 16 3. METODOLOGÍA DEL PROCESAMIENTO DE IMÁGENES .............................. 20 3.1 Vasos Sanguíneos ........................................................................................ 20 3.1.1 Canal G con CLAHE adaptativo ........................................................... 21 3.1.2 Ajuste de intensidad ............................................................................. 24 3.1.3 Mínimo regional .................................................................................... 24 3.1.4 Reconstrucción morfológica ................................................................. 26 3.1.5 BottomHat............................................................................................. 28 3.1.6 Filtro de Gauss ..................................................................................... 29 3.1.7 Binarización .......................................................................................... 30 3.2 Disco óptico y fóvea ...................................................................................... 31 3.2.1 Disco óptico .......................................................................................... 31 3.2.2 Fóvea ................................................................................................... 36 3.3 Exudados ...................................................................................................... 38 3.3.1 Canal verde, filtro de media y ajuste de iluminación de fondo .............. 38 3.3.2 Binarización con Otsu adaptado y contraste adaptativo ....................... 40 - 7 - 3.3.3 Dilatación y reconstrucción morfológica por dilatación ......................... 40 3.4 Microaneurismas y hemorragias sanguíneas ............................................... 42 3.4.1 Ajuste de iluminación ............................................................................ 42 3.4.2 Mínimo regional y reconstrucción morfológica ...................................... 42 3.4.3 Segmentación de prelesiones y eliminación de vasos sanguíneos y fóvea................ .............................................................................................. 43 3.4.4 Dilatación y reconstrucción morfológica ............................................... 43 3.4.5 Binarización .......................................................................................... 43 4. CLASIFICACIÓN DE RETINOPATÍA ................................................................. 47 4.1 Clasificador ................................................................................................... 47 4.4.1 Máquinas de soporte vectorial (SVM) ................................................... 47 4.4.2 Red neuronal (ANN) ............................................................................. 49 4.2 Interfaz gráfica .............................................................................................. 50 4.3 Resultados .................................................................................................... 51 5. CONCLUSIONES .............................................................................................. 57 BIBLIOGRAFIA ...................................................................................................... 58 ANEXOS ................................................................................................................ 65spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleDiseño de un sistema automático para determinar el grado de progresión de retinopatía diabética utilizando técnicas de visión artificialspa
dc.title.translatedDesign of an automatic system to determine the degree of progression of diabetic retinopathy using artificial vision techniqueseng
dc.degree.nameIngeniero Mecatrónicospa
dc.coverageBucaramanga (Colombia)spa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.programPregrado Ingeniería Mecatrónicaspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsMechatronic Engineeringeng
dc.subject.keywordsComputer visioneng
dc.subject.keywordsArtificial intelligenceeng
dc.subject.keywordsMedicineeng
dc.subject.keywordsBiomedical engineeringeng
dc.subject.keywordsApparatus and instrumentseng
dc.subject.keywordsInvestigationseng
dc.subject.keywordsAnalysiseng
dc.subject.keywordsDiabetic retinopathyeng
dc.subject.keywordsAutomationeng
dc.subject.keywordsArtificial visioneng
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
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dc.contributor.cvlacGonzález Acevedo, Hernando [0000544655]*
dc.contributor.googlescholarGonzález Acevedo, Hernando [V8tga0cAAAAJ&hl=es]*
dc.contributor.scopusGonzález Acevedo, Hernando [55821231500]*
dc.contributor.researchgateGonzález Acevedo, Hernando [Hernando-Gonzalez]*
dc.subject.lembIngeniería mecatrónicaspa
dc.subject.lembVisión por computadorspa
dc.subject.lembInteligencia artificialspa
dc.subject.lembMedicinaspa
dc.subject.lembIngeniería biomédicaspa
dc.subject.lembAparatos e instrumentosspa
dc.subject.lembInvestigacionesspa
dc.subject.lembAnálisisspa
dc.description.abstractenglishDiabetic retinopathy is one of the most common conditions of diabetes [1], being the third cause of irreversible blindness in the world, but the first in people of productive age (16 to 64 years) who suffer from type 1 or type diabetes 2 for more than 10 years, which have a 50% to 60% chance of developing it. In order to identify the medical condition under a physical observation of the ocular organ, a slit lamp examination or a fundus camera with dilated pupils is performed [2]. These forms of diagnosis are time consuming and often require fluorescein angiography or optical coherence tomography to confirm the degree of diabetic retinopathy.eng
dc.subject.proposalRetinopatía diabéticaspa
dc.subject.proposalAutomatizaciónspa
dc.subject.proposalVisión artificialspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.contributor.researchgroupGrupo de Investigación Control y Mecatrónica - GICYMspa
dc.contributor.researchgroupGrupo de Investigaciones Clínicasspa
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa


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