Using an Image Fusion Methodology to Improve Efficiency and Traceability of Posterior Pole Vessel Analysis by ROPtool



Sasapin.G. Prakalapakorn1, *, Laura A. Vickers1, Rolando Estrada2, Sharon F. Freedman1, Carlo Tomasi2, Sina Farsiu1, 3, David K. Wallace1
1 Deptartment of Ophthalmology, Duke University, Durham, NC 27710, USA
2 Deptartment of Computer Science, Duke University, Durham, NC 27708, USA
3 Deptartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA


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© 2017 Prakalapakorn et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Duke University Eye Center, DUMC 3802, 2351 Erwin Road, Durham, NC, 27710, USA; Tel: 919-684-3764, Fax: 919-684-6096, E-mail: Grace.Prakalapakorn@duke.edu


Abstract

Background:

The diagnosis of plus disease in retinopathy of prematurity (ROP) largely determines the need for treatment; however, this diagnosis is subjective. To make the diagnosis of plus disease more objective, semi-automated computer programs (e.g. ROPtool) have been created to quantify vascular dilation and tortuosity. ROPtool can accurately analyze blood vessels only in images with very good quality, but many still images captured by indirect ophthalmoscopy have insufficient image quality for ROPtool analysis.

Purpose:

To evaluate the ability of an image fusion methodology (robust mosaicing) to increase the efficiency and traceability of posterior pole vessel analysis by ROPtool.

Materials and Methodology:

We retrospectively reviewed video indirect ophthalmoscopy images acquired during routine ROP examinations and selected the best unenhanced still image from the video for each infant. Robust mosaicing was used to create an enhanced mosaic image from the same video for each eye. We evaluated the time required for ROPtool analysis as well as ROPtool’s ability to analyze vessels in enhanced vs. unenhanced images.

Results:

We included 39 eyes of 39 infants. ROPtool analysis was faster (125 vs. 152 seconds; p=0.02) in enhanced vs. unenhanced images, respectively. ROPtool was able to trace retinal vessels in more quadrants (143/156, 92% vs 115/156, 74%; p=0.16) in enhanced mosaic vs. unenhanced still images, respectively and in more overall (38/39, 97% vs. 34/39, 87%; p=0.07) enhanced mosaic vs. unenhanced still images, respectively.

Conclusion:

Retinal image enhancement using robust mosaicing advances efforts to automate grading of posterior pole disease in ROP.

Keywords: Image analysis, Retinopathy of prematurity, ROPtool, Vessel analysis.