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Probabilistic labelling for enhancement of vessel networks applied to retinal images

Paredes Soto, Daniel Alonso (2016) Probabilistic labelling for enhancement of vessel networks applied to retinal images. PhD thesis, University of Sheffield.

Text (Probabilistic labelling for enhancement of vessel networks applied to retinal images)
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Occlusive vascular disease affecting arterial circulations is the major and fastest growing health problem worldwide, and underlies common conditions such as heart attack, stroke and peripheral vascular disease. Although vascular diseases may be assessed according to clinical history, screening may be required to evaluate health conditions or courses of treatment. Vasculature in the retina and other organs such as the brain have similar anatomical properties and regulatory mechanisms. Changes in the morphology of retinal vasculature may be associated with vascular-related conditions such as hypertension and stroke. Owing to its high cost-effectiveness, eye fundus photography is often used to study changes in the retinal vasculature. This research proposes a probabilistic pixel labelling method based on analysis of local and global features of the image to enhance the detail of vessel structures. Our approach produces a probability map that could be further used by contextual approaches (e.g. Markov Random Fields) for segmenting vessel networks as future application. We first correct contrast variation due to non-uniform illumination and reflections produced by eye tissue using statistical methods to locally estimate the contrast behind vasculature structures. Our labelling method is based on the Hessian matrix to locally estimate the maximum probability of the principal local curvature—given by eigenvalues—matching an ideal vessel curvature. We defined a realistic model based on imaging physics to produce the ideal vessel curvature governed by the Beer-Lambert Law for estimating the absorption of energy as it is propagated through uniformly filled objects. The local maximum posterior probability—based on Bayes’ rule—was eventually estimated by combining the prior (using the proposed background estimation) and the likelihood produced by Monte Carlo simulations. The proposed method in this research was compared with one of the most popular vessel detectors due to Frangi showing similar results.

Item Type: Thesis (PhD)
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Electronic and Electrical Engineering (Sheffield)
Identification Number/EthosID: uk.bl.ethos.693099
Depositing User: Mr Daniel Alonso Paredes Soto
Date Deposited: 02 Sep 2016 15:49
Last Modified: 12 Oct 2018 09:26
URI: http://etheses.whiterose.ac.uk/id/eprint/13800

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