Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

Oscar Perdomo; John Arevalo; Fabio A. González

doi:10.1117/12.2285964

17 November 2017 Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

Oscar Perdomo, John Arevalo, Fabio A. González

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 105721G (2017) https://doi.org/10.1117/12.2285964
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

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.

Citation Download Citation

Oscar Perdomo, John Arevalo, and Fabio A. González "Combining morphometric features and convolutional networks fusion for glaucoma diagnosis", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105721G (17 November 2017); https://doi.org/10.1117/12.2285964

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