Retinal image analysis: Concepts, applications and potential
Introduction
The retina is the only location where blood vessels can be directly visualised non-invasively in vivo. Increasing technology leading to the development of digital imaging systems over the past two decades has revolutionised fundal imaging. Whilst digital imaging does not still have the resolution of conventional photography, modern digital imaging systems offer very high-resolution images that are sufficient for most clinical scenarios (Facey et al., 2002; Fransen et al., 2002; Hansen et al., 2004b; Klein et al., 2004a; van Leeuwen et al., 2003). In addition, digital imaging has the advantage of easier storage on media that do not deteriorate in quality with time, can be transmitted over short distances throughout a clinic or over large distances via electronic transfer (allowing expert “at-a-distance” opinion in large rural communities), can be processed to improve image quality, and subjected to image analysis to perform objective quantitative analysis of fundal images and the potential for automated diagnosis. In the research or screening setting, large databases of fundal images may be automatically classified and managed more readily than labour-intensive observer-driven techniques. Automated diagnosis may also aid decision-making for optometrists.
In this review, we outline the principles upon which retinal digital image analysis is based. We discuss current techniques used to automatically detect landmark features of the fundus, such as the optic disc, fovea and blood vessels. We review the use of image analysis in the automated diagnosis of pathology (with particular reference to diabetic retinopathy). We also review its role in defining and performing quantitative measurements of vascular topography, how these entities are based on ‘optimisation’ principles and how they have helped to describe the relationship between systemic cardiovascular disease and retinal vascular changes. We also review the potential future use of fundal image analysis in telemedicine. Whilst this technology can be employed for other image acquisition modalities, such as confocal scanning laser ophthalmoscopes (cSLO) (Deckert et al., 2005), ultrasound (Schachar and Kamangar, 2005) and optical coherence tomography (Yanuzzi et al., 2004), in this article we concentrate solely on image processing based on fundal colour photography and fluorescein angiography.
Section snippets
Principle of digital image capture, processing and analysis
Digital images are made up in such a way that makes them accessible to simple and complex mathematical manipulation. For black and white images (grey scale) at any given locus of pixel, typically there is a corresponding intensity on a range from 0 (black) to 255 (white) (28 for 8-bit images) {for 12-bit images, there are 4096 grey levels (212), etc.}. Hence, the image is composed of an array of pixels of varying intensity across the image, the intensity corresponding to the level of “greyness”
Automated localisation (segmentation) of retinal landmarks
A potential use of fundal digital image analysis is the ability to analyse a large database of fundal images in a short period of time. The identification of fundal landmark features such as the optic disc, fovea and the retinal vessels as reference co-ordinates is a prerequisite before systems can achieve more complex tasks identifying pathological entities. Reliable techniques exist for identification of these structures in retinal photographs.
Automated detection of pathology using retinal digital image analysis
Automated diagnosis of retinal fundal images using digital image analysis offers huge potential benefits. In a research setting, it offers the potential to examine a large number of images with time and cost savings and offer more objective measurements than current observer-driven techniques. Advantages in a clinical context include the potential to perform large numbers of automated screening for conditions such as diabetic retinopathy, and hence to reduce the workload required from manual
Quantitative measurements from Fundal images
An important role of retinal digital image analysis is the ability to perform quantitative objective measurements from retinal colour photographs. However, the effect of image magnification resultant from fundal photography has to be overcome, either incorporating an adjusted measurement to take the magnification into account, or to use dimensionless measurements so that results between patients can be compared.
Digital retinal vascular image analysis and telemedicine
Because ophthalmology is largely dependent on visual information, it is an ideal specialty for telemedicine (Constable et al., 2000; Lamminen et al., 2003; Murdoch, 1999; Yogesan et al., 1998). Digital capture of images and the potential for transmission of these images via electronic transfer across large distances with subsequent image analysis offers the potential for more efficacious use of medical resources in large, rural communities that may otherwise have difficulty obtaining expert
Future directions
Retinal digital image analysis is able to exploit the ease with which the retinal circulation can be visualised, photographed, and analysed non-invasively in vivo. Using objective, quantitative measures from retinal vasculature which are based on principals of optimisation of a branching vasculature, studies have been able to improve our understanding of the effect of systemic factors on the microvasculature. The most commonly performed quantitative measurement from digital retinal vascular
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