Abstract
A large number of human retinal diseases are characterized by a progressive loss of cones, the photoreceptors critical for visual acuity and color perception. Adaptive Optics (AO) imaging presents a potential method to study these cells in vivo. However, AO imaging in ophthalmology is a relatively new phenomenon and quantitative analysis of these images remains difficult and tedious using manual methods. This paper illustrates a novel semi-automated quantitative technique enabling registration of AO images to macular landmarks, cone counting and its radius quantification at specified distances from the foveal center. The new cone counting approach employs the circle Hough transform (cHT) and is compared to automated counting methods, as well as arbitrated manual cone identification. We explore the impact of varying the circle detection parameter on the validity of cHT cone counting and discuss the potential role of using this algorithm in detecting both cones and rods separately.
Highlights
Retinal diseases are a significant cause of progressive deterioration of visual function and collectively, they blind millions of people every year [1]
The development of scanning laser ophthalmoscopes and optical coherence tomography systems that can capture en face, threedimensional images have led to enhanced contrast in image details and the ability to analyze cross-sections of the retina, respectively [2,3,4,5]
In addition to the above functions, we demonstrate that our customized software is able to identify cones reliably using the circle Hough transform algorithm [21,22,23,24,25] implemented in Matlab; the cHT searches for circular formations of a given radius in the image
Summary
Retinal diseases are a significant cause of progressive deterioration of visual function and collectively, they blind millions of people every year [1]. Accurate diagnosis and monitoring of retinal diseases is heavily reliant on high-resolution retinal imaging, in addition to standard visual acuity testing, examination and other functional assessments including microperimetry and electroretinography. Significant advances have been made in the capability of retinal imaging over the past several decades through the introduction of digital retinal cameras and fluorescein angiography for visualizing retinal vasculature. Borrowed from astrophysics, AO technology enables the visualization of individual retinal photoreceptor cells, retinal blood vessel capillaries and bundles of ganglion cell axons within the living human retina [6,7,8]
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