Estimation of retinal vessel caliber using model fitting and random forests

Teresa Araújo; Ana Maria Mendonça; Aurélio Campilho

doi:10.1117/12.2252025

3 March 2017 Estimation of retinal vessel caliber using model fitting and random forests

Teresa Araújo, Ana Maria Mendonça, Aurélio Campilho

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101341K (2017) https://doi.org/10.1117/12.2252025
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Retinal vessel caliber changes are associated with several major diseases, such as diabetes and hypertension. These caliber changes can be evaluated using eye fundus images. However, the clinical assessment is tiresome and prone to errors, motivating the development of automatic methods. An automatic method based on vessel crosssection intensity profile model fitting for the estimation of vessel caliber in retinal images is herein proposed. First, vessels are segmented from the image, vessel centerlines are detected and individual segments are extracted and smoothed. Intensity profiles are extracted perpendicularly to the vessel, and the profile lengths are determined. Then, model fitting is applied to the smoothed profiles. A novel parametric model (DoG-L7) is used, consisting on a Difference-of-Gaussians multiplied by a line which is able to describe profile asymmetry. Finally, the parameters of the best-fit model are used for determining the vessel width through regression using ensembles of bagged regression trees with random sampling of the predictors (random forests). The method is evaluated on the REVIEW public dataset. A precision close to the observers is achieved, outperforming other state-of-the-art methods. The method is robust and reliable for width estimation in images with pathologies and artifacts, with performance independent of the range of diameters.

Conference Presentation

Citation Download Citation

Teresa Araújo, Ana Maria Mendonça, and Aurélio Campilho "Estimation of retinal vessel caliber using model fitting and random forests", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341K (3 March 2017); https://doi.org/10.1117/12.2252025

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