Morphometric measurements of the retinal vasculature in ultra-wide scanning laser ophthalmoscopy as biomarkers for cardiovascular disease

Abstract

Retinal imaging enables the visualization of a portion of the human microvasculature in-vivo and non-invasively. The scanning laser ophthalmoscope (SLO), provides images characterized by an ultra-wide field of view (UWFoV) covering approximately 180-200º in a single scan, minimizing the discomfort for the subject. The microvasculature visible in retinal images and its changes have been vastly investigated as candidate biomarkers for different types of systemic conditions like cardiovascular disease (CVD), which currently remains the main cause of death in Europe. For the CARMEN study, UWFoV SLO images were acquired from more than 1,000 people who were recruited from two studies, TASCFORCE and SCOT-HEART, focused on CVD. This thesis presents an automated system for SLO image processing and computation of candidate biomarkers to be associated with cardiovascular risk and MRI imaging data. A vessel segmentation technique was developed by making use of a bank of multi-scale matched filters and a neural network classifier. The technique was devised to minimize errors in vessel width estimation, in order to ensure the reliability of width measures obtained from the vessel maps. After a step of refinement of the centrelines, a multi-level classification technique was deployed to label all vessel segments as arterioles or venules. The method exploited a set of pixel-level features for local classification and a novel formulation for a graph cut approach to partition consistently the retinal vasculature that was modelled as an undirected graph. Once all the vessels were labelled, a tree representation was adopted for each vessel and its branches to fully automate the process of biomarker extraction. Finally, a set of 75 retinal parameters, including information provided by the periphery of the retina, was created for each image and used for the biomarker investigation.

Date of Award	2016
Original language	English
Supervisor	Manuel Trucco (Supervisor) & Graeme Houston (Supervisor)