Asl, ME, Koohbanani, NA, Frangi, AF orcid.org/0000-0002-2675-528X et al. (1 more author) (2017) Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform. Journal of Medical Imaging, 4 (3). 034006. ISSN 2329-4302
Abstract
Extraction of blood vessels in retinal images is an important step for computer-aided diagnosis of ophthalmic pathologies. We propose an approach for blood vessel tracking and diameter estimation. We hypothesize that the curvature and the diameter of blood vessels are Gaussian processes (GPs). Local Radon transform, which is robust against noise, is subsequently used to compute the features and train the GPs. By learning the kernelized covariance matrix from training data, vessel direction and its diameter are estimated. In order to detect bifurcations, multiple GPs are used and the difference between their corresponding predicted directions is quantified. The combination of Radon features and GP results in a good performance in the presence of noise. The proposed method successfully deals with typically difficult cases such as bifurcations and central arterial reflex, and also tracks thin vessels with high accuracy. Experiments are conducted on the publicly available DRIVE, STARE, CHASEDB1, and high-resolution fundus databases evaluating sensitivity, specificity, and Matthew’s correlation coefficient (MCC). Experimental results on these datasets show that the proposed method reaches an average sensitivity of 75.67%, specificity of 97.46%, and MCC of 72.18% which is comparable to the state-of-the-art.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017, Society of Photo-Optical Instrumentation Engineers (SPIE). This is an author produced version of a paper published in the Journal of Medical Imaging. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | diameter estimation; Gaussian process; Radon transform; retinal imaging; vessel tracking |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Sep 2018 13:54 |
Last Modified: | 05 Sep 2018 09:48 |
Status: | Published |
Publisher: | Society of Photo-optical Instrumentation Engineers |
Identification Number: | 10.1117/1.JMI.4.3.034006 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135023 |