Smartphone-Based Accurate Analysis of Retinal Vasculature towards Point-of-Care Diagnostics.

Xiayu Xu,Xuemin Wang,Ruofan Cao,Peilin Lv,Maiye Zhang,Feng Xu,Wenxiang Ding

doi:10.1038/srep34603

Xiayu Xu, Xuemin Wang + Show 5 more

Open Access

https://doi.org/10.1038/srep34603

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Abstract

Retinal vasculature analysis is important for the early diagnostics of various eye and systemic diseases, making it a potentially useful biomarker, especially for resource-limited regions and countries. Here we developed a smartphone-based retinal image analysis system for point-of-care diagnostics that is able to load a fundus image, segment retinal vessels, analyze individual vessel width, and store or uplink results. The proposed system was not only evaluated on widely used public databases and compared with the state-of-the-art methods, but also validated on clinical images directly acquired with a smartphone. An Android app is also developed to facilitate on-site application of the proposed methods. Both visual assessment and quantitative assessment showed that the proposed methods achieved comparable results to the state-of-the-art methods that require high-standard workstations. The proposed system holds great potential for the early diagnostics of various diseases, such as diabetic retinopathy, for resource-limited regions and countries.

Highlights

Retinal vasculature changes have been associated with various eye diseases and systemic diseases, which manifest themselves on the retina by altering vessel topological features, such as vessel width and tortuosity[1,2,3,4,5,6]
There are a few attempts in the establishment of a portable POC diagnostic system[12,26], they were only tested on public databases consisting of high-quality fundus images captured with DF-cameras
The desired algorithms for a POC diagnostic system should be accurate and robust enough to work at resource-limited settings, and fast and simple enough to be implemented in a portable device

Summary

Introduction

Retinal vasculature changes have been associated with various eye diseases (e.g., diabetic retinopathy) and systemic diseases (e.g., hypertension), which manifest themselves on the retina by altering vessel topological features, such as vessel width and tortuosity[1,2,3,4,5,6]. We developed a fully automatic retinal image analysis system that can deal with low quality images captured with a HF-camera combined with a smartphone (iExaminer, Welch-Allyn Inc., Skaneateles Fall, NY, USA). This system can read a fundus image, segment retinal vessels, analyze individual vessel width, and store or uplink the results. We developed a visual saliency based vessel segmentation method and a graph-theoretic vessel width measurement method These two methods were compared with existing methods on high quality fundus images, and tested on low quality clinical images taken with a smartphone. The proposed system was implemented independently in a smartphone app, which provides a user-friendly interface for image acquisition, test analysis, and result management

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