Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

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Main Authors: Claro,Maíla, Santos,Leonardo, Silva,Wallinson, Araújo,Flávio, Moura,Nayara, Macedo,André
Format: Digital revista
Language:English
Published: Centro Latinoamericano de Estudios en Informática 2016
Online Access:http://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S0717-50002016000200005
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spelling oai:scielo:S0717-500020160002000052016-09-01Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature ExtractionClaro,MaílaSantos,LeonardoSilva,WallinsonAraújo,FlávioMoura,NayaraMacedo,André Classification feature extraction Glaucoma segmentation The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracyinfo:eu-repo/semantics/openAccessCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal v.19 n.2 20162016-08-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S0717-50002016000200005en
institution SCIELO
collection OJS
country Uruguay
countrycode UY
component Revista
access En linea
databasecode rev-scielo-uy
tag revista
region America del Sur
libraryname SciELO
language English
format Digital
author Claro,Maíla
Santos,Leonardo
Silva,Wallinson
Araújo,Flávio
Moura,Nayara
Macedo,André
spellingShingle Claro,Maíla
Santos,Leonardo
Silva,Wallinson
Araújo,Flávio
Moura,Nayara
Macedo,André
Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction
author_facet Claro,Maíla
Santos,Leonardo
Silva,Wallinson
Araújo,Flávio
Moura,Nayara
Macedo,André
author_sort Claro,Maíla
title Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction
title_short Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction
title_full Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction
title_fullStr Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction
title_full_unstemmed Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction
title_sort automatic glaucoma detection based on optic disc segmentation and texture feature extraction
description The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy
publisher Centro Latinoamericano de Estudios en Informática
publishDate 2016
url http://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S0717-50002016000200005
work_keys_str_mv AT claromaila automaticglaucomadetectionbasedonopticdiscsegmentationandtexturefeatureextraction
AT santosleonardo automaticglaucomadetectionbasedonopticdiscsegmentationandtexturefeatureextraction
AT silvawallinson automaticglaucomadetectionbasedonopticdiscsegmentationandtexturefeatureextraction
AT araujoflavio automaticglaucomadetectionbasedonopticdiscsegmentationandtexturefeatureextraction
AT mouranayara automaticglaucomadetectionbasedonopticdiscsegmentationandtexturefeatureextraction
AT macedoandre automaticglaucomadetectionbasedonopticdiscsegmentationandtexturefeatureextraction
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