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Proceedings Paper

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation
Author(s): José Ignacio Orlando; Marcos Fracchia; Valeria del Río; Mariana del Fresno
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Paper Abstract

Several ophthalmological and systemic diseases are manifested through pathological changes in the properties and the distribution of the retinal blood vessels. The characterization of such alterations requires the segmentation of the vasculature, which is a tedious and time-consuming task that is infeasible to be performed manually. Numerous attempts have been made to propose automated methods for segmenting the retinal vasculature from fundus photographs, although their application in real clinical scenarios is usually limited by their ability to deal with images taken at different resolutions. This is likely due to the large number of parameters that have to be properly calibrated according to each image scale. In this paper we propose to apply a novel strategy for automated feature parameter estimation, combined with a vessel segmentation method based on fully connected conditional random fields. The estimation model is learned by linear regression from structural properties of the images and known optimal configurations, that were previously obtained for low resolution data sets. Our experiments in high resolution images show that this approach is able to estimate appropriate configurations that are suitable for performing the segmentation task without requiring to re-engineer parameters. Furthermore, our combined approach reported state of the art performance on the benchmark data set HRF, as measured in terms of the F1-score and the Matthews correlation coefficient.

Paper Details

Date Published: 17 November 2017
PDF: 13 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057210 (17 November 2017); doi: 10.1117/12.2283539
Show Author Affiliations
José Ignacio Orlando, Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina)
Pladema Institute (Argentina)
Univ. Nacional del Centro de Buenos Aires (Argentina)
Marcos Fracchia, Univ. Nacional del Centro de Buenos Aires (Argentina)
Valeria del Río, Univ. Nacional del Centro de Buenos Aires (Argentina)
Mariana del Fresno, Pladema Institute (Argentina)
Univ. Nacional del Centro de Buenos Aires (Argentina)
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (Argentina)


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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