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Proceedings Paper

Combining morphometric features and convolutional networks fusion for glaucoma diagnosis
Author(s): Oscar Perdomo; John Arevalo; Fabio A. González
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Paper Abstract

Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.

Paper Details

Date Published: 17 November 2017
PDF: 6 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105721G (17 November 2017); doi: 10.1117/12.2285964
Show Author Affiliations
Oscar Perdomo, Univ. Nacional de Colombia (Colombia)
John Arevalo, Univ. Nacional de Colombia (Colombia)
Fabio A. González, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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