Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Masoud E. Asl; Navid A. Koohbanani; Alejandro F. Frangi; Ali Gooya

doi:10.1117/1.JMI.4.3.034006

12 September 2017 Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform

Masoud E. Asl, Navid A. Koohbanani, Alejandro F. Frangi, Ali Gooya

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 3, 034006 (September 2017). https://doi.org/10.1117/1.JMI.4.3.034006

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.

Citation Download Citation

Masoud E. Asl, Navid A. Koohbanani, Alejandro F. Frangi, and Ali Gooya "Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform," Journal of Medical Imaging 4(3), 034006 (12 September 2017). https://doi.org/10.1117/1.JMI.4.3.034006

Received: 24 January 2017; Accepted: 9 August 2017; Published: 12 September 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available