Automatic identification of diabetic maculopathy stages using fundus images.
J Med Eng Technol
; 33(2): 119-29, 2009.
Article
em En
| MEDLINE
| ID: mdl-19205991
ABSTRACT
Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Reconhecimento Automatizado de Padrão
/
Aumento da Imagem
/
Retinopatia Diabética
/
Macula Lutea
Tipo de estudo:
Diagnostic_studies
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Guideline
/
Prognostic_studies
Limite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
J Med Eng Technol
Ano de publicação:
2009
Tipo de documento:
Article
País de afiliação:
Índia
País de publicação:
ENGLAND
/
ESCOCIA
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GB
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GREAT BRITAIN
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INGLATERRA
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REINO UNIDO
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SCOTLAND
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UK
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UNITED KINGDOM