Literature DB >> 21690008

Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs.

Meindert Niemeijer1, Xiayu Xu, Alina V Dumitrescu, Priya Gupta, Bram van Ginneken, James C Folk, Michael D Abramoff.   

Abstract

A decreased ratio of the width of retinal arteries to veins [arteriolar-to-venular diameter ratio (AVR)], is well established as predictive of cerebral atrophy, stroke and other cardiovascular events in adults. Tortuous and dilated arteries and veins, as well as decreased AVR are also markers for plus disease in retinopathy of prematurity. This work presents an automated method to estimate the AVR in retinal color images by detecting the location of the optic disc, determining an appropriate region of interest (ROI), classifying vessels as arteries or veins, estimating vessel widths, and calculating the AVR. After vessel segmentation and vessel width determination, the optic disc is located and the system eliminates all vessels outside the AVR measurement ROI. A skeletonization operation is applied to the remaining vessels after which vessel crossings and bifurcation points are removed, leaving a set of vessel segments consisting of only vessel centerline pixels. Features are extracted from each centerline pixel in order to assign these a soft label indicating the likelihood that the pixel is part of a vein. As all centerline pixels in a connected vessel segment should be the same type, the median soft label is assigned to each centerline pixel in the segment. Next, artery vein pairs are matched using an iterative algorithm, and the widths of the vessels are used to calculate the AVR. We trained and tested the algorithm on a set of 65 high resolution digital color fundus photographs using a reference standard that indicates for each major vessel in the image whether it is an artery or vein. We compared the AVR values produced by our system with those determined by a semi-automated reference system. We obtained a mean unsigned error of 0.06 (SD 0.04) in 40 images with a mean AVR of 0.67. A second observer using the semi-automated system obtained the same mean unsigned error of 0.06 (SD 0.05) on the set of images with a mean AVR of 0.66. The testing data and reference standard used in this study has been made publicly available.

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Mesh:

Year:  2011        PMID: 21690008     DOI: 10.1109/TMI.2011.2159619

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  24 in total

1.  Retinal vessel detection and measurement for computer-aided medical diagnosis.

Authors:  Xiaokun Li; William G Wee
Journal:  J Digit Imaging       Date:  2014-02       Impact factor: 4.056

2.  OCT feature analysis guided artery-vein differentiation in OCTA.

Authors:  Minhaj Alam; Devrim Toslak; Jennifer I Lim; Xincheng Yao
Journal:  Biomed Opt Express       Date:  2019-03-26       Impact factor: 3.732

3.  Simultaneous arteriole and venule segmentation with domain-specific loss function on a new public database.

Authors:  Xiayu Xu; Rendong Wang; Peilin Lv; Bin Gao; Chan Li; Zhiqiang Tian; Tao Tan; Feng Xu
Journal:  Biomed Opt Express       Date:  2018-06-15       Impact factor: 3.732

4.  Retinal Artery-Vein Classification via Topology Estimation.

Authors:  Rolando Estrada; Michael J Allingham; Priyatham S Mettu; Scott W Cousins; Carlo Tomasi; Sina Farsiu
Journal:  IEEE Trans Med Imaging       Date:  2015-06-10       Impact factor: 10.048

5.  Artery/vein classification of retinal vessels using classifiers fusion.

Authors:  Samra Irshad; Xiao-Xia Yin; Yanchun Zhang
Journal:  Health Inf Sci Syst       Date:  2019-11-08

6.  Color Fundus Image Guided Artery-Vein Differentiation in Optical Coherence Tomography Angiography.

Authors:  Minhaj Alam; Devrim Toslak; Jennifer I Lim; Xincheng Yao
Journal:  Invest Ophthalmol Vis Sci       Date:  2018-10-01       Impact factor: 4.799

7.  Automated detection of malarial retinopathy-associated retinal hemorrhages.

Authors:  Vinayak S Joshi; Richard J Maude; Joseph M Reinhardt; Li Tang; Mona K Garvin; Abdullah Abu Sayeed; Aniruddha Ghose; Mahtab Uddin Hassan; Michael D Abràmoff
Journal:  Invest Ophthalmol Vis Sci       Date:  2012-09-25       Impact factor: 4.799

8.  Automated classification and quantitative analysis of arterial and venous vessels in fundus images.

Authors:  Minhaj Alam; Taeyoon Son; Devrim Toslak; Jennifer I Lim; Xincheng Yao
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2018-02-19

9.  Differential Artery-Vein Analysis Improves the Performance of OCTA Staging of Sickle Cell Retinopathy.

Authors:  Minhaj Alam; Jennifer I Lim; Devrim Toslak; Xincheng Yao
Journal:  Transl Vis Sci Technol       Date:  2019-03-26       Impact factor: 3.283

10.  Combining ODR and Blood Vessel Tracking for Artery-Vein Classification and Analysis in Color Fundus Images.

Authors:  Minhaj Alam; Taeyoon Son; Devrim Toslak; Jennifer I Lim; Xincheng Yao
Journal:  Transl Vis Sci Technol       Date:  2018-04-18       Impact factor: 3.283

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