Hyperspectral Imaging of the Eye
for Early Detection of Retinal Diseases
I. Al Abboud
E. Theofanidou
A. R. Harvey*
Engineering and Physical Sciences
Heriot Watt University
Edinburgh, UK
*a.r.harvey@hw.ac.uk
Abstract Hyperspectral imaging (HSI) holds great promise for the detection and
classification of several diseases, especially in the field of optical biopsy as applied to
oncology, and functional retinal imaging in ophthalmology. In essence, hyperspectral
imaging of the biochemistry of the retina offers a powerful, non-invasive tool for the
investigation and diagnosis of retinal diseases such as diabetic retinopathy, agerelated macular degeneration and glaucoma. The eye presents a unique opportunity
for direct imaging of the vasculature, but also presents a challenging set of problems;
such as imaging an erratically moving and photosensitive structure through a low
numerical aperture and in the presence of high levels of clutter. Sequentional spectral
imaging using a liquid crystal tuneable filter (LCTF) has been achieved with wide
spectral range, reasonable SNRs and lower light intensity at the retina. The
calibration and modification of a commercial fundus camera has been achieved,
which enables the recording of high quality spectral images of the retina. Collected
retinal images enable the estimation of the diameter of veins and arteries, and
haemoglobin oxygenation which provides useful information in the diagnosis of
certain diseases.
1. Introduction
Hyperspectral imaging entails capturing a spectrum for every pixel within a scene.
Hyperspectral data cube is constructed with three dimensions; two spatial dimensions (X, Y),
and wavelength λ. Using a two-dimensional detector array, it is usual that one of three
dimensions is obtained through time sequential scanning. The increased spectral information
recorded provides enhanced capabilities over conventional imaging techniques for the
purposes of classifying and quantifying the concentration of chemicals.
2. Spectral retinal imaging
Hyperspectral imaging of the biochemistry of the retina offers potential as a powerful noninvasive tool for the investigation and diagnosis of retinal diseases. For example, using HSI to
monitor the changes in oxygen concentration in the veins and arteries can enable the detection
of diabetic retinopathy in its early stages. Conventional retinal imaging techniques like fundus
imaging, fluorescein angiogram, and optical coherence tomography (OTC) provide
information about the circulatory system and the condition of the back of the eye (retinal
structure) not the chemistry (e.g. oxygen saturation). Several techniques have been reported
for spectral retinal imaging, these include: full-field Fourier-transform spectral imaging; one-
dimensional, Fourier-transform spectral imaging1; and one-dimensional dispersive spectral
imaging2. We have developed an instrument that employs time-sequential, full field spectral
imaging in which spectral filtering of the source is accomplished using a liquid crystal
tuneable filter. In comparison to other techniques, this provides the advantages of lower light
intensity at the eye, two-dimensional imaging as required for screening purposes and a
minimum number of recorded image frames. Minimisation of light intensity and the number
of image frames recorded is important for patient comfort, which in turn improves the quality
of images recorded. Although a snap-shot technique3, (currently also under development,)
offers the ideal solution for use as a clinical tool and for improved spectral calibration, the
time-sequential instrument described here offers the advantage of a more flexible approach to
record spectral images of the retina.
3. Developed instrument
 A liquid crystal tuneable filter has been incorporated into a conventional fundus
camera to enable computer-controlled, random-access spectral filtering of the source
(figure 1) with 10nm Spectral resolution.
 Reimaging of the conventional output image has enabled the 60° field of view to be
retained.
 Interfacing with a computer to acquire the images and control the filter through
LabView.
 12-Bit Images captured using a cooled, low-noise CCD camera.
LCTF
Figure 1: Modified optical system
4. Pre-processing and calibration
Recorded images need to be calibrated and co-registered due to non uniform illumination and
random movement of the eye and reflections from the lens surface which cause high intensity
artefacts in the retinal image. Narrow-band spectral images are mutually co-registered to
correct for rotational and translational offsets introduced by movement of the eye between
images. A relatively simple cross-correlation technique has been found to enable accurate
coregistration. Prior to registration, images are spatially filtered to enhance blood vessels
since these serve as good spectrally invariant landmarks across the spectral bands. Filtering
consists of the following steps which were optimized heuristically; band pass filtering,
matched filtering and edge detection. A typical set of images taken from a spectral data cube
is shown in figure 2 for wavelengths between 590 nm and 600 nm. This illustrates the
relationship between optical density and oxygenation: the optical density (OD) of oxygenated
blood in the larger arteries varies strongly across this wavelength range, whereas the OD of
deoxygenated blood in the veins remains high.
590nm
596nm
600nm
Figure 2: Set of retinal images at different wavelengths
5. Retinal Oximetry
Measuring oxygen saturation in the fundus of the human eye could aid the diagnosis and
monitoring of disorders for early detection of some diseases. Oximetric measurements can be
implemented by exploitation of the spectral variation of optical densities. Using LambertBeer’s law and the well-known absorption spectra of oxygenated and de-oxygenated
haemoglobin4 and considering multiple optical paths as depicted in figure 3.
Figure 3: Optical paths
Self calibration is needed to compensate for uneven illumination. This is accomplished by
linear fitting to estimate the intensity on the both sides of vessel figure 4.
Figure 4: Self calibration
Nonlinear fitting of the vessel OD profile enables an estimate of the vessel diameter at each
wavelength and hence of blood oxygenation as shown in figures 5 and 6.
Linear fitting
Optical density
Fitted model
Figure 5: Vessel profile and fitted model
OS=95%
. . .....real values
___ fitted model.
¬OS=20%
¬OS=99%
Figure 6: results from applying nonlinear fitting to find OS
6. Conclusions and future work
We report here a new instrument for the recording of spectral images of the retina.
Spectral processing methods (Spectral Angle Mapper; SAM, linear spectral unmixing)
provide a useful semi-quantitative map of retinal biochemistry; quantitative mapping
requires a rigorous model of light propagation in the retina. We have discussed how a
relatively simple model can enable oximetry within blood vessels. Future work will
concentrate on refining the model to enable sufficient accuracy to be obtained and the
implementation of a snapshot spectral imaging technique for improved convenience and
accuracy.
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SPIE,4132, p. 356-364,NOV 2000.
[2] M. Hammer, D. Schweitzer, L .Leistritz, M .Scibor,K. Donnerhacke, and J. Strobel,
Imaging spectroscopy of the human ocular fundus in vivo, journal of biomedical optics
2(4), 418–425 (1997) .
[3] A. S. Gorman, A. R. Harvey, Snapshot spectral imaging using image replication and
polarising interferometry, photon06, 2006.
[4] V. Assendelft, O.W, Spectrophotometry of haemoglobin derivatives (Charles C.
Thomas, Springfield, IL, 1970).