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Automatic extraction of vascularity measurements using OCT-A images
Author(s) -
Macarena Díaz,
Jorge Novo,
Manuel G. Penedo,
Marcos Ortega
Publication year - 2018
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2018.07.261
Subject(s) - computer science , vascularity , optical coherence tomography , artificial intelligence , computer vision , fundus (uterus) , image processing , coherence (philosophical gambling strategy) , modality (human–computer interaction) , pattern recognition (psychology) , radiology , image (mathematics) , medicine , mathematics , statistics
Optical Coherence Tomography Angiography (OCT-A) represents a new modality of ophthalmological imaging that stands out for being a non-invasive capture technique that facilitates the analysis of the vascular characteristics of the eye fundus. In this paper, we propose a complete automatic methodology that identifies the vascular and avascular zones in OCT-A images, quantifying each one of them for their posterior use in clinical analyses and diagnostic processes. To achieve this, we firstly intensify the vascular characteristics to facilitate the posterior extraction. Then, a set of image processing techniques are combined to differentiate both vascular and avascular regions and, finally, measure their representative parameters. The proposed methodology was tested on a set of images that were marked by an expert ophthalmologist, being used as reference in the validation of the method. The proposed approach presented satisfactory results in the validation experiments with the vascular and avascular measurements, demonstrating their utility for the diagnosis and monitoring of different vascular diseases that are frequently analysed through the retinal microcirculation.

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