Iterative variational mode decomposition based automated detection of glaucoma using fundus images.
Comput Biol Med
; 88: 142-149, 2017 09 01.
Article
en En
| MEDLINE
| ID: mdl-28728059
ABSTRACT
Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Interpretación de Imagen Asistida por Computador
/
Glaucoma
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Técnicas de Diagnóstico Oftalmológico
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Fondo de Ojo
Tipo de estudio:
Diagnostic_studies
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Guideline
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Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Comput Biol Med
Año:
2017
Tipo del documento:
Article