Department of engineering hull uni

Department of Engineering
University of Hull
Final Year Project
2006/2007
ANALYSIS OF RETINAL IMAGES FOR
HEALTHCARE
Paul Francis
BEng Mechanical Engineering
First Supervisor: Mr. G.L. Cutler
Second Supervisor: Dr. J.M. Gilbert
3rd
May 2007

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Abstract
Accurate measurement of vessel structures in retinal images plays an important role in
diagnosing cardiovascular diseases. This paper presents a method for the direct quantification of
vessel geometry and texture in retinal images associated with increased oral intake of vitamin C.
Using models of vessel intensity profile presented and applied across a series of time lapse
images, results are presented for variation in vessel geometry and texture. The developed
methods were used to analyse retinal vessel variation across images taken over a 6 month
period of a healthy white male taking oral supplementation of vitamin C. This study found that
direct quantification of variation across images was achieved using the models of vessel intensity
profile, and that variation across images was effected by machine accuracy, Image capture and
vessel segmentation techniques. In conclusion, more accurate quantification of the changes
witnessed requires the enhancement of existing instrumentation and diagnostic techniques to
facilitate the increase in accuracy in the measurement of arterial deposits via retinal image
analysis.

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Acknowledgements
Many thanks to all involved in this project in particular Mr. Gavin Cutler the project
supervisor and Dr. Sydney Bush without whom this project would not have been possible.

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Contents
Page No.
1 Introduction 10
1.1 Objectives and limitations 10
1.2 Structure of the thesis 11
2 Review of previous work 12
3 Description of methods and procedures 24
3.1 Image capture 24
Figure 3.1.1 Sample retinal image 24
3.2 Image processing 25
3.3 Image segmentation 25
Figure 3.3.1 Area of interest 25
3.4 Image analysis 25
3.5 Data analysis 26
Figure 3.5.1 Position of datum on vessel 26
Figure 3.5.2 Vessel area of interest 27
Figure 3.5.3 3-Dimensional plot of vessel intensity fit 28
Figure 3.5.4 Calibration scale for gray-scale intensity 29
4 Results 30
Figure 4.1 Gray-scale intensity plot pre-normalisation 30
Figure 4.2 Gray-scale intensity plot post-normalisation 30
Table 4.1 Gray-scale intensity association to pulse cycle 31
Figure 4.3 Gray-scale intensity profile with variation in pulse cycle 31
Figure 4.4 Red colour intensity plot 32
Figure 4.5 Green colour intensity plot 32
Figure 4.6 Blue colour intensity plot 32
Figure 4.7 Gray-scale intensity profiles pre-normalisation 33
Figure 4.8 Gray-scale intensity profiles post-normalisation 33
Table 4.2 Gray-scale intensity associated to vit-C supplementation 34
Figure 4.9 Gray-scale intensity associated to vit-C supplementation 34
5 Project management summary 35
5.1 Project management 35
5.2 GANTT Charts 38
Figure 5.2.1 Semester 1 GANTT Chart 38
Figure 5.2.2 Semester 2 GANTT Chart 38
Figure 5.2.3 Semester 2 Revised GANTT Chart 39

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6 Discussion 40
7 Conclusions 43
8 Future work 44
9 References 45
10 Appendices 46
11 Publicity page 50

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Glossary of Terms
Absorption - The loss of light of certain wavelengths as it passes through a material and is
converted to heat or other forms of energy.
Aqueous - The clear fluid occupying the space between the cornea and the lens of the eye.
Algorithm - A set of well-defined rules or procedures for solving a problem in a finite number of
steps.
Arteriolar - Are small diameter blood vessel that extends and branches out from an artery and
leads to capillaries.
Arteriovenous - Of, relating to, or connecting both arteries and veins.
Atherosclerosis - Is a disease affecting the arterial blood vessel, commonly referred to as a
"hardening" or "furring" of the arteries. It is caused by the formation of multiple plaques within the
arteries.
AVR - Arteriolar to venular diameter ratio
Bit Map - A representation of graphics or characters by individual pixels arranged in rows and
columns. Black and white require one bit, while high definition color up to 32.
Cardioretinometry® - is the rapid assessment of the changing health of the cardiovascular
system by the sequential study of digital retinal images captured by non-mydriatic fundus
cameras.
Cardiovascular - The term cardiovascular refers to the heart (cardio) and the blood vessels
(vascular). The cardiovascular system includes arteries, veins, arterioles, venules, and
capillaries.
Cerebrovascular - Of or relating to the blood vessels that supply the brain.
Charged-Coupled Device (CCD) - Technology for making semiconductor devices (including
image sensors).
Choroid - The layer of blood vessels that lies between the retina and the sclera. The choroid
nourishes the back of the eye.

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Contrast Enhancement - Stretching of the gray-level values between dark and light portions of
an image to improve both visibility and feature detection.
Diabetic Retinopathy - Changes in the retina due to diabetes. Adverse changes in the retinal
blood vessels leads to weakening and eventually to more serious eye disorders. In its most
advanced stages, diabetic retinopathy can lead to severe vision loss or blindness.
Digital Ocular Imaging - A digital camera used for taking anterior and posterior images of the
eye.
Edge Detection - The ability to determine the edge of an object.
Feature Extraction - Determining image features by applying feature detectors to distinguish or
segment them from the background.
Fluoroscein Angiography/Ocular Angiography - A diagnostic procedure used to diagnose and
localize leaky blood vessels in the eye.
Fovea - The center area of the retina that receives the focus of an object.
Glaucoma - A progressive disease caused by increased intraocular pressure (IOP), that results
from an over-production of fluid or malfunction in the eye’s drainage structures. Glaucoma can
lead to vision loss. The most common form is open angle glaucoma, caused by aqueous fluid
building up in the anterior chamber. Closed angle glaucoma occurs when abnormal structures in
the front of the eye, known as the angel, are too narrow. This results in a smaller channel for the
aqueous to pass through. If aqueous becomes blocked, IOP increases.
Gray-Scale - Variations of values from white, through shades of gray, to black in a digitized
image with black assigned the value of zero and white the value of one.
Gray-scale Image - An image consisting of an array of pixels which can have more than two
values. Typically, up to 256 levels (8 bits) are used for each pixel.
HSI - Hue, saturation, and intensity; a colour system.
Hypertensive - Causing in an increase in blood pressure.
Intensity - The relative brightness of a portion of the image or illumination source.
Ischemic - Oxygen-deprived.

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Machine Vision - The use of devices for optical non-contact sensing to automatically receive
and interpret an image of a real scene, in order to obtain information and/or control machines or
processes.
Macula - A small, highly sensitive part of the retina responsible for detailed central vision.
Macular Degeneration - Also known as age-related macular degeneration. A disease affecting
the central area of the retina (the macula), which over time can cause a partial or complete loss
of central vision.
Microaneurysm - Dilation of the wall of a capillary, characteristic of certain disease entities.
Microvascular - The smallest of blood vessels in the body.
Morphology - Mathematics of shape analysis. An algebra whose variables and shapes and
whose operations transform those shapes.
Ophthalmologist - Physician and surgeon specializing in the structure functions and diseases of
the eye.
Optical Coherence Tomography (OCT) - A non-invasive technology that creates a high-
resolution color image of the eye using light and light rays instead of ultrasound. OCT is used to
measure the thickness of the macula, the tissue make-up of the nerve fiber layer or to analyze
individual layers of the retina.
Pixel - Acronym for picture element. The individual elements in a digitized image array.
Polarized Light - Light which has had the vibrations of the electric or magnetic field vector
typically restricted to a single direction in a plane perpendicular to its direction of travel. It is
created by a type of filter which absorbs one of the two perpendicular light rays. Crossing
polarizers theoretically blocks all light transmission.
Retina - A very thin layer of light-sensitive tissues that line the inner part of the eye. It is
responsible for capturing the light rays that enter the eyes, and along with the optic nerve,
converting them to light impulses and sending them to the brain for processing.
RGB - Red, Green, and Blue; a colour system
Sclera - The tough, opaque tissue that serves as the eye’s protective outer layer.

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Specular Reflection - Light rays that are highly redirected at or near the same angle of
incidence to a surface. Observation at this angle allows the viewer to "see" the light source.
Vascular Occlusion - Also known as Retina Vein Occlusion. A condition in which a retinal vein
becomes obstructed by a blood vessel, which results in a hemorrhages in the retina. This can
lead to swelling and lack of oxygen in the retina. The sudden onset of blurred vision or a missing
area of vision characterizes a Branch Vein Occlusion. A Central Vein Occlusion results in severe
loss of central vision.
Vasculature - Arrangement of blood vessels in the body or in an organ or body part.
Venules - a small blood vessel that allows deoxygenated blood to return from the capillary beds
to the larger blood vessels called veins.

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1. Introduction
Analysis of the retinal vessel structure is of immense interest in the investigation of
diseases that involve structural or functional changes in the vasculature. The retinal blood
vessels are available to non-invasive visualisation, and therefore provide a unique opportunity to
directly observe and study the structure of the circulation in vivo. New clinical studies suggest
that narrowing of the retinal blood vessels may be an early indicator of cardiovascular diseases.
This project aims to further develop the methodology for the analysis of digital retinal images in
collaboration with a local ophthalmic opticians practice with a view to the enhancement of
existing instrumentation and diagnostic techniques to facilitate the measurement of arterial
deposits and indirectly monitor patient’s general cardiovascular health through routine non-
invasive examination. A review of literature associated to this area of study is included in this
paper and details key aspects of past, present, and future methods proposed to quantify
vasculature changes
The research presented in this paper is a part of a larger effort investigating how changes
in retinal vasculature are associated to dietary conditions and how the inclusion of vitamin C
supplements in the diet may have an effect on this. The project involves, in addition to the
University Of Hull, Dr. Sydney Bush who is a local optometrist specialising in the detection of
cardiovascular disease through routine eye examination. Dr. Bush believes changes in retinal
vasculature can be directly related to supplementation of vitamin C whereby cholesterol deposits
and vessel narrowing are seen to gradually disappear with the ingestion of regular dietary
supplements of vitamin C. This project aims to develop a methodology by which the changes
witnessed can be directly quantified. This work will be beneficial to society as a whole as
cardiovascular disease rates are high and increasing at a dramatic pace, thus any new research
potentially leading to a reduction in cardiovascular disease must be welcomed.
1.1 Objectives and limitations
The main objective of this research was to develop a methodology for the analysis of
digital retinal images, with particular attention to changes in the geometry and texture of blood
vessels. Many methods have been developed for the measurement of vessel diameters and this
project proposes a modelling approach of vessel intensity profiles to quantify changes witnessed
in clinical studies. A secondary objective was the enhancement of existing instrumentation and
diagnostic techniques to facilitate the measurement of arterial deposits via automated image
analysis.

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The time available for this research was six months and due to this limitation only key
areas are included in this study. Another major limitation was the age and accuracy of the
equipment used and restrictions due to hardware and software used during this project.
1.2 Structure of the thesis
This thesis consists of seven main sections. Section 2 provides a literature review of
previous work closely related to this area of study. Section 3 details the methods and procedures
used to acquire and analyse retinal images during this project. Section 4 presents the results
obtained from the study. A project management summary is presented in section 5 which
includes project GANTT charts used as a means of tracking progress. Sections 6, 7, & 8 provide
discussions, conclusions, and the potential for future work respectively.

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2. Review of previous work
This section presents a literature review and evaluation of the current techniques used to
detect features of the fundus. The use of image analysis in the automated diagnosis of pathology
is also reviewed as well as the future potential of fundal image analysis in medical research.
Contents:
2.1 Retinal image analysis: Concepts, applications and potential
Figure 2.1 Retinal vessel intensity profile and tracking process
2.2 Retinal image analysis using machine vision
2.3 Measurement of vessel diameters on retinal images for cardiovascular studies
Figure 2.3 Intensity distribution curves using twin Gaussian functions
2.4 Characterisation of changes in blood vessel width and tortuosity in retinopathy of
prematurity using image analysis
2.5 Are retinal arteriolar or venular diameters associated with markers for cardiovascular
disorders? The Rotterdam study
2.6 Variation associated with measurement of retinal vessels diameters at different points in
the pulse cycle
Figure 2.6 Results table for variation across images
2.7 Theoretical relations between light streak characteristics and optical properties of retinal
vessels
Figure 2.7 Central light reflex model
2.8 Retinal micro vascular abnormalities and their relationship with hypertension,
cardiovascular disease and mortality
2.9 Comparative study of retinal vessel segmentation methods on a new publicly available
database

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2.1 Retinal image analysis: concepts, applications and potential
American Journal of Ophthalmology, Volume 141, Issue 3, March 2006, Page 603
N. Patton, T.M. Aslam, T. MacGillivray, I.J. Deary, B. Dhillon, R.H. Eikelboom, K.
Yogesan and I.J. Constable
In this review the concepts, applications and potential of retinal image analysis are
discussed. This review outlines the principles upon which digital retinal analysis is based. The
report discusses current techniques used to automatically detect landmark features of the
fundus, such as the optic disc, fovea and blood vessels. The use of image analysis in the
automated diagnosis of pathology is reviewed and 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. The potential future use of fundal image analysis is also reviewed
in this paper.
This paper discusses retinal vascular segmentation techniques which utilize the contrast
existing between the retinal blood vessel and surrounding background, as shown in ‘Figure 1’.
Another technique for vessel segmentation include ‘vessel tracking’, whereby vessel center
locations are automatically sought over each cross section of a vessel along the vessels
longtitudinal axis, having been given a start and an end point. Vessel tracking can provide very
accurate measurements of vessel width and tortuosity.
Figure 2.1: Retinal vessel intensity profile and tracking process (Patton et al 2006)
This review concludes, “With an increasingly aged population and increased strain on
medical resources, the use of strategies such as telemedicine and widespread screening of
individuals at risk of certain diseases will increase”.

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2.2 Retinal image analysis using machine vision
Department of information technology, Lappeenranta University of technology, June 6
2005.
Markku Kuivalainen.
In this review the author attempts to develop reliable and accurate image processing and
pattern recognition methods for automatic fundus image analysis. The topic of this paper is
studying how lesions in retina caused by diabetic retinopathy can be detected from colour fundus
images by using machine vision methods. “Methods for equalising uneven illumination in fundus
images, detecting regions of poor image quality due to inadequate illumination, and recognising
abnormal lesions were developed during this work”. In this masters thesis the main focus is on
accurate and reliable detection of abnormal lesions, belonging to diabetic retinopathy, from
colour fundus images. The author discusses the main concepts of retinal image analysis as well
as giving a broad overview of image processing techniques. The techniques and tools developed
in this work were used by an ophthalmologist who marked lesions in the images to help in the
development and evaluation. The abnormality detection process consists of image segmentation
and candidate lesion classification. In addition to thresholding, two novel methods were used in
the segmentation of images: A circular filter-based method for detecting small lesions, and a
morphology-based method for haemorrhage detection. Segmented candidate lesions were then
classified into lesions and non-lesions by using a simple rule-based classifier. The main body of
this thesis is a study of diabetic retinopathy; however the methods used to detect abnormalities in
retinal image analysis are similar to the techniques being used in this final year project.
Equalisation of uneven illumination was found to be the key issue for the success of the
research. The results proved that “it is possible to use algorithms for assisting an ophthalmologist
to segment fundus images into normal parts and lesions, and thus support the ophthalmologist in
their decision making”. The algorithm developed in this study detects regions where the image
quality is inadequate, and therefore these regions are left unprocessed thus highlighting the
areas which are unsatisfactory for evaluation.

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2.3 Measurement of vessel diameters on retinal images for cardiovascular
studies
Department of Clinical Pharmacology, Imperial College School …, 2001 - cs.bham.ac.uk
X Gao, A Bharath, A Stanton, A Hughes, N Chapman
In this study, a method of vessel diameter measurement has been developed
incorporated with a tracking technique. Twin Gaussian functions are introduced to model the
distribution of grey level profile over a vessel cross section. This tracking technique is utilized to
study the variations of vessel diameter in the direction of vessel longitude axis. This technique
enabled the measurement of an average diameter over any length of a vessel.
Figure 2.3: Intensity distribution curves using twin Gaussian functions (Gao et al. 2001)
This study concludes that “the model of twin Gaussian functions not only gives excellent
performance in fitting the intensity profile of over a cross section of a vessel, but also has theory
in line with the findings by other researchers”. It develops simple relationships between vessel
width and the intensity distribution parameters.

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2.4 Characterization of changes in blood vessel width and tortuosity in
retinopathy of prematurity using image analysis
Medical Image Analysis, Volume 6, Issue 4, December 2002, Pages 407-429
Conor Heneghan, John Flynn, Michael O’Keefe and Mark Cahill
This paper represents a general technique for segmenting out vascular structures in
retinal images, and characterising the segmented blood vessels. The segmentation technique
used by this study consists of several steps including morphological pre-processing to emphasise
linear structures such as vessels, followed by a final morphological filtering stage. Thresholding
of images is used to provide a segmented vascular mask, then this mask is skeletonised in order
to allow identification of points in the image where vessels cross therefore allowing the widths
and tortuosity of vessels to be calculated. In this review they show that segmentation of the
vascular structure in retinal images is possible by use of a combination of morphological and
linear filtering. “The quality of the segmentation is shown to be dependant on a number of
parameters such as image quality, choice of threshold, and choice of structuring elements.
Successful segmentation allowed a variety of further processes to be studied such as: Visual
highlights of vessels in the image, accurate characterisation of vessel parameters such as
thickness and tortuosity, and location of vessel bifurcation and crossings which can act as
intrinsic features for registration schemes”.
Using these methods Henegan et al conclude that only the change in width was
statistically significant in the study and that factors confounding a more accurate test include poor
image quality, inaccuracies in vessel segmentation, inaccuracies in measurement of vessel width
and tortuosity, and they found limitations inherent in screening based solely on examination of
the posterior pole.

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2.5 Are retinal arteriolar or venular diameters associated with markers for
cardiovascular disorders? The Rotterdam study
Investigative Opthalmology & Visual Science, Volume 45, No.7, July 2004
M. Kamran Ikram, Frank Jan de Jong, Johannes R. Vingerling, Jacqueline C. M.
Witteman, Albert Hofman, Monique M. B. Breteler and Paulus T. V. M. de Jong
The purpose of this study was to evaluate the effects of decreasing retinal arteriolar and
venular diameters and lower retinal arteriolar to venular ratio (AVR). It is suggested that a lower
AVR reflects general arteriolar narrowing and predicts the risk of cardiovascular diseases. The
Rotterdam study takes into consideration, on one hand the AVR, and on the other hand blood
pressure, atherosclerosis, inflammation markers and cholesterol levels.
The methods used in this study were the analysis of retinal arteriolar and venular diameters
and measures of baseline blood pressures, cholesterol levels, and markers of arthereosclerosis
and inflammation were also measured.
The results of this study show that “with increasing blood and pulse pressures, retinal arteriolar
and venular diameters and the AVR decreased significantly and linearly. Lower arteriolar
diameters were associated with increased carotid intima-media thickness. Larger venular
diameters were associated with higher carotid plaque score, more aortic calcifications, lower
ankle-arm index, higher leukocyte count, higher erythrocyte sedimentation rate, higher total
serum cholesterol, lower HDL, higher waist to hip ratio, and smoking”. The study also found that
“a lower AVR was related to increased carotid intima-media thickness, higher carotid plaque
score, higher leukocyte count, lower HDL, higher body mass index, higher waist to hip ratio, and
smoking”.
The Rotterdam study used data captured from 5674 individuals less than 55 years of age and
concludes that because larger venular diameters are associated with atherosclerosis ,
inflammation, and cholesterol levels, the AVR does not depend only on generalised arteriolar
narrowing.

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2.6 Variation associated with measurement of retinal vessel diameters at
different points in the pulse cycle
2004 - bjo.bmjjournals.com
MD Knudtson, BEK Klein, R Klein, TY Wong
M.D. Knudston et al discuss the effects of variations in measurements of retinal vessel
diameters at different points in the pulse cycle. This study used a healthy white male aged 19
years and digitised images were taken at three distinct points in the pulse cycle using a pulse
synchronised ear clip trigger device used to capture images at the desired points in the pulse
cycle. “Two trained graders measure the retinal vessel diameter of one large arteriole, one large
venule, one small arteriole, and one small venule 10 times in each of the 30 images taken over a
one hour period”. This scientific report claims “Across images taken at the same point in the
pulse period the change from the minimum to maximum measurement was between 6% and
17% for arterioles and between 2% and 11% for venules”. This report establishes an extremely
important factor in the measurement of vessels in retinal image analysis. ‘Figure 3’ shows the
variation across images.
Of great importance to this project are the findings that “The largest source of variation we
found was across photographic images”. These findings were carefully considered during the
analysis of the time lapse images upon which this project is based as the images used are similar
in nature to the images used in this report and therefore display similar variation.
Figure 2.6: Results table for variation across images (Knudston et al. 2004)

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2.7 Theoretical relations between light streak characteristics and optical
properties of retinal vessels
ACTA OPTHALMOLOGICA, 1986, VOL 179, P33-37
O. Brinchmann-Hansen and Halvor Heier.
In this paper the ‘Central light reflex’ which can be seen on the centre of larger vessels in
retinal images are analysed and discussed. This characteristic can have an effect on the
accurate measurement of blood vessels in retinal images. The simplified model used in this
paper can be seen in ‘Figure 1’. This report concludes that; “The light reflex must be generated
from a rough reflecting surface, and the intravascular column of erythrocytes is probably the main
surface in question. Extravascular conditions might possibly change the characteristics of this
reflection. Changes in density and thickness of the vessel wall will not influence the width of the
light streak, while this is expected to increase the intensity of the reflection”. The findings in this
review are crucial to the analysis of images in this project as the ‘Central light reflex’ can clearly
be seen on the images which are being analysed and therefore the factors set out in this paper
have been taken into consideration when evaluating changes in the texture and geometry of
blood vessels.
This report also states that “If arteriosclerosis changes the refractive index of the vessel
wall, we start to get reflections from the two surfaces of the wall, or within the wall, which add to
the reflex stripe, but no changes in width will occur. A change in the intensity of the reflex may
therefore indicate a change in the refractive index of one or more of the structures overlying the
blood column”.
In this review a simple model is used to calculate effects of changes in refractive indices
and anatomical sizes of various structures surrounding the blood column in a retinal vessel. This
model is shown in ‘Figure 4’. A number of steps are performed in the calculations used in this
review and are listed below:
1. Assume values of Do, Di, Wo, Nw, Nv.
2. Choose a ray from the centre of the pupil making a small angle ‘u’ with the axis ‘CO’.
3. By means of Snell’s law of refraction and the law of reflection, the path of the ray is found
up to the point where it passes close to the edge of the light source.
4. If the ray does not hit the edge, we choose another angle ‘u’ of the initial part of the ray
and return to step 3.
5. When we have found a ray passing sufficiently close to the edge, the first segment of the
ray is extended until it intersects the reference plane in point ‘P’. We then have PO = Wr/2
where Wr is the width of the reflex.

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6. Return to step 1 to find Wr for other values of the parameters Do, Di, …..etc.
Study of the resulting curves show that the reflex size is accurately given by the expression:
Wr = 0.075 Wo
Where Wr is the reflex width, and Wo is the diameter of the blood column.
Figure 2.7: Central light reflex model (Brinchmann-Hansen et al. 1986)
The following parameters are used in the calculations:
Do – Outer diameter of the vessel wall
Di – Inner diameter of the vessel wall
Wo – Diameter of the blood column
Tp – Thickness of the plasma zone
Nv – Index of refraction of the vitreous
Nw – Index of refraction of the wall
Np – Index of refraction of the plasma

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2.8 Retinal Microvascular Abnormalities and their Relationship with
Hypertension, Cardiovascular Disease, and Mortality
Survey of Ophthalmology, Volume 46, Issue 1, July-August 2001, Pages 59-80
Tien Yin Wong, Ronald Klein, Barbara E. K. Klein, James M. Tielsch, Larry Hubbard and
F. Javier Nieto
This study represents an overview of previous clinical studies carried out around the
world and their relationship with hypertension, cardiovascular disease, and mortality. In this
review, retinal microvascular abnormalities or characteristics are used to include all retinal
microvascular pathology. “Retinal arteriolar changes refer to those abnormalities related to the
retinal arterioles only, such as generalized and focal arteriolar narrowing, and arteriovenous (AV)
nicking. Retinopathy is used to include all microvascular characteristics not explicitly arteriolar in
nature, such as retinal hemorrhages, microaneurysms, cotton-wool spots, hard exudates,
macular edema and optic disc swelling”. This study details findings on the relationship between
changes in retinal vasculature and atherosclerosis, ischemic heart disease, and stroke and
suggests that the relationship between retinal microvascular abnormalities and artherosclerosis is
weak, as most of the studies have drawn conclusions based on indirect and circumstantial
associations between these abnormalities and either risk factors for atherosclerosis or
cardiovascular disease secondary to artherosclerosis rather than on the direct quantification of
artherosclerosis itself. This report establishes inconsistencies in previous work and large scale
studies such as the Beaver Dam, ARIC, Framingham eye study, and Blue Mountains eye study,
and discusses the accuracy of these studies with respect to the methods used to identify
abnormalities.
This review concludes that “many of the historical studies were inadequate, and that
current data suggests that retinal microvascular abnormalities, as detected by retinal
photography in a research setting, are related independently to past blood pressure levels and
risk of stroke. In contrast, the relationship with other cardiovascular disease is fairly inconsistent,
and further inference is limited at this time”. They also conclude that “direct opthalmoscopic
examination by physicians is to unreliable to be of clinical value, particularly in the detection of
subtle retinal microvascular changes”. This study suggests that clearly, well-designed
prospective studies using objective methods to determine retinal characteristics, and both
subclinical and clinical cardiovascular end points, are needed to address these issues before
retinal lesions are ultimately used for cardiovascular risk stratification and screening.
This report suggests that automated, computer based imaging systems appear to hold
much promise in the near future for the more accurate detection of disease at a premature stage.

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In conclusion, retinal microvascular abnormalities are common in the adult non-diabetic
population. “Retinopathy is associated with severe hypertensive end-organ damage, but is
absent in the majority of people with well controlled blood pressure. Generalised retinal arteriolar
narrowing and arteriovenous nicking appear to be irreversible long term markers of mild to
moderate hypertension, related not only to current and past blood pressure levels, but to
cerebrovascular diseases as well”.

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2.9 Comparative study of retinal vessel segmentation methods on a new
publicly available database
Images Sciences Institute, Univ. Medical Centre Utrecht, Utrecht, The Netherlands.
M. Niemeijer, J. Staal, B. van Ginneken, M. Loog and M. D. Abramoff
This study compares the performance of a number of vessel segmentation algorithms
using data from a newly constructed publicly available retinal image database (DRIVE). Five
different vessel segmentation methods were tested on the DRIVE database. The first a matched
filter approach notes that the gray-level profiles of the cross-sections of retinal vessels have an
intensity profile which can be approximated by a Gaussian using a 2-Dimensional matched filter
approach in order to detect the vessels. The second method reviewed is scale-space analysis
and region growing approach. This method uses a combination of scale space analysis and
region growing to segment the vasculature. “Two features are used to characterize the blood
vessels, the gradient magnitude of the image intensity and the ridge strength both at different
scales. The ridge strength is determined by calculating the absolute largest eigen value of the
second order derivatives of the image intensity. To account for the difference in vessel width
across the retina both these features are normalized by the scale s over the scale-space while
retaining only the local maxima. The histograms of both features are used in the final region-
growing step, in which the image pixels are divided into two classes, vessel and non-vessel. This
is accomplished by alternating the vessel and background region growing and lowering the
feature thresholds after each iteration, this continues until no new pixels are added to either of
the two classes”. The third method reviewed uses mathematical morphology, this algorithm
consists of 3 steps, firstly recognition of linear parts by computing the supremum of openings
using a linear structuring element at different orientations. Secondly, noise suppression by using
a geodesic reconstruction of the supremum openings into the original image. And finally, removal
of different types of undesirable patterns by applying the laplacian on the result of the previous
step followed by a specially designed alternating filter. The final result can then be thresholded to
produce a segmentation of the vasculature.
The main focus of attention in the study is on a pixel classification approach similar to the
approach used in this project, In this study, the pixel classification method was deemed to be the
more accurate of the 5 methods employed, however it is very labor intensive and therefore on
larger databases may prove to be un-workable, though for the purpose of this study and due to
the small amount of images studied it was deemed acceptable.

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3. Description of the methods and procedures
3.1 Image capture
The first stage in retinal image analysis is image capture. This was carried out using a
TOPCON TRC-NW5S Non-mydriatic retinal camera, with a SONY 3CCD colour video camera
attachment. The digital camera uses a charge-coupled device as a direct digital sensor. The
charge coupled device is an array of tiny light sensitive diodes which convert the light signals into
electrical charges and creates an array of pixels. At each pixel in the array, the electrical current
proportional to the analogue light level is converted into a digital level. The camera attachment
used with this equipment has a resolution of 768 x 564 pixels. The retinal images used in this
project were taken from a 30 year old healthy white male and were taken by a qualified
optometrist at a local opticians practise. A retinal image is shown below in ‘Figure 3.1.1’:
Figure 3.1.1: Sample retinal image
A series of retinal images were taken from both eyes including a series of pulse cycle
related images taken at varying points in the pulse cycle. Images were taken initially on the 11th
October 2006, and then a further set on the 12th
March 2007, followed by a final set taken on the
20th
April. The annotated images were then stored in a database where subsequent time lapse
analysis could be performed on these images to satisfy the objectives of this project.

25
3.2 Image processing
Image processing operations transform the gray-scale values of the pixels. The aims of
processing of an image usually fall into three main categories: enhancement, restoration, and
segmentation. This project uses image segmentation as the primary processing technique.
3.3 Image segmentation
Segmentation involves the division of images into smaller sections that are of particular
interest. For this project an area of interest was selected which included a large vessel within the
area. The images were rotated through 35° Clockwise so as to display the vessel running
through the area of interest in a vertical direction for ease of subsequent analysis. The area of
interest used for analysis of all the images was a section 12 pixels wide in the horizontal direction
(x), and 20 pixels long in the vertical direction (y), with the vessel running vertically through the
centre of the area.
Figure 3.3.1: Area of interest
In order to normalise the images a second area of interest was selected in each of the
images. This area was taken from the gray-scale intensity profile which appears alongside the
retinal image and is created automatically by the equipment used. The area of interest is taken
from the top of the scale in the centre and is 6 pixels wide in the horizontal direction (x), and 10
pixels long in the vertical direction (y). From this area of interest statistical analysis can be
applied to the returned values of gray-scale intensity and a mean value derived by which all the
images can be adjusted to.
3.4 Image analysis
One of the main objectives of this project was to develop a methodology by which the
project partner Dr. Sydney Bush would be able to quantify changes which can be visually seen
on a computer monitor in vessel geometry and texture, thus allowing him to prescribe dietary

26
changes and vitamin supplementation to patients, giving him the ability to quantify changes
displayed in the fundus images of patients over a period of time.
3.5 Data analysis
The methods used to carry out the detailed analysis of a series of time lapse images
during this project are detailed in this section. The sequence of operations has been split into a
number of different steps with illustrations to identify the processes involved:
1. The annotated images are copied onto floppy disks:
Due to the limitations of the hardware and the restrictions of the software the only way to acquire
images from the system being used was to create an annotated image and copy it onto a floppy
disk in a TIFF format. The media was later transferred to a memory stick to ensure compatibility
with other project PC’s. Due to the nature of TIFF files generally no quality loss would be
encountered due to the edit and re-save cycles and the images have high quality smooth colour
variations.
2. The images are opened up in a digital image editing software suite (COREL Photo paint)
in a 24 bit RGB colour mode. The image size is 800x600 pixels.
3. Rulers are created around the perimeter of the image and the rulers are broken down into
single pixel spacing.
4. An area of specific interest is then selected and this area is then magnified x10.
5. The image is then rotated through a user defined amount in order for the vessel of
interest to run vertically in the image.
6. A branch closest to this area is then selected and the central point of the branch where
the 3 vessels meet is then selected to be used as a datum point from which to take
measurements. See ‘Figure 3.5.1’.
Figure 3.5.1: Position of datum in vessel

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7. Guidelines are then set-up on the image to encapsulate the specific area of interest.
These guidelines form a box around the area of interest 20 pixels in length by 12 pixels
wide. See ‘Figure 3.5.2’.
Figure 3.5.2: Vessel area of interest
8. Using the ‘image info’ function on the software and changing the display settings to:
• Primary – 24 Bit RGB
• Secondary – 8 Bit Gray-scale
Each individual pixel in the boxed area has a value for Red, Green, and Blue colour intensity
between 0 and 255, and a value for its gray-scale intensity between 0 and 256.
9. Then a histogram of each individual horizontal block 1 pixel long by 12 pixels wide is
created and the data for each of these lines is stored in a matrix for further evaluation at a
later stage.
10. Once all the data from the boxed area has been gathered and inputted into a matrix of
data then a graphical representation can be made of the colour intensities using this data
and the end result creates a profile of the vessel with respect to its intensity. See ‘Figure
3.5.3’.

28
Figure 3.5.3: 3-Dimensional plot of vessel intensity fit
11. A second time lapse image is then selected and steps 2 to 10 are then repeated in order
to create a second profile of the same vessel.
12. These profiles are then displayed on the same graph and any differences in intensity can
be clearly seen. (See Results section)
13. Once data has been gathered from all the images of interest a normalisation method must
be employed in order to normalise the intensities to give a true comparison of data
gathered. The processes involved in normalisation are set out in steps 14 to 16 below:
14. In each of the images captured by the retinal camera a gray-scale intensity chart is
displayed alongside the retinal image. See ‘Figure 3.5.4’. From the intensity charts for
each of the images studied an individual overall image intensity can be found. Once
evaluated the gray-scale intensity can then be used to normalise the data values for
intensity collected from steps 1 to 12.
Figure 3.5.4: Calibration scale for gray-scale intensity

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15. Guidelines are set-up on the image to encapsulate the specific area of interest in the
centre and at the top of the gray-scale chart. These guidelines form a box around the
area of interest 10 pixels in length by 6 pixels wide. See ‘Figure 3.5.4’.
16. The gathered data from the area of interest displayed in ‘Figure 3.5.4’ can then be
statistically analysed to offer a true reflection of the variation across time lapse images.
(See Results section)

30
4. Results
Images were obtained from a healthy white male aged 30 years who was taking vitamin C
(sodium l-ascorbate) at a prescribed ½ gram oral intake 6 times daily. The images used for this
project were taken by a trained professional at a local ophthalmic practise under standard eye
examination conditions. Sets of images were taken on day 1, day 152, and day 191 of the project
from both eyes collectively. The images taken on day 1 incorporate a set of pulse cycle related
images which were taken by the trained professional at 4 distinct points in the pulse cycle.
40
50
60
70
80
90
100
1234567891011121314
15
16
17
18
19
20
2 3 4 5 6 7 8 9 10 11 12
Intensity
Distance
in
Pixels
Y
Distance in Pixels X
Point 1
Point 2
Point 3
Point 4
Figure 4.1: Gray-scale intensity plot pre-normalisation
40
50
60
70
80
90
100
1234567891011121314
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16
17
18
19
20
2 3 4 5 6 7 8 9 10 11 12
Intensity
Distance
in
Pixels
Y
Point 1
Point 2
Point 3
Point 4
Figure 4.2: Gray-scale intensity plot post-normalisation
Figure 4.1 illustrates a
surface plot of the 4 different
points in the pulse cycle
across the vessel which is
being studied. The variation
between each of the plots is
quite significant and this is
what is witnessed when
simply comparing images on
a PC monitor, hence leading
to visual differences being
recognised.
Figure 4.2 illustrates how the
variation decreases in surface
plots of the 4 different points in
the pulse cycle across the vessel
which is being studied. This
graph displays the same 4 points
in the pulse cycle as Figure 4.1
however in this graph the data
has been normalised in
collaboration with the calibration
gray-scale included in every
image.

31
Normalisation was applied to the 4 plots of different points in the pulse cycle in order to
get a true reflection of the changes witnessed. After normalisation had taken place the data from
each of the pulse cycle images was analysed and the results are shown in ‘Table 4.1’.
Table 4.1: Gray-scale intensity associated to variations in pulse cycle
From the 4 different points in the pulse cycle an 8% change in mean intensity is
witnessed between point 1 and point 2. This is the maximum source of variability within these
results and will be taken into account when quantifying changes across time lapse images.
0
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12
Distance in Pixels
Intensity
Point 1 Point 2 Point 3 Point 4
Figure 4.3: Gray-scale intensity profile with variation due to pulse cycle
Pulse point Mean Standard Deviation Minimum Maximum % Change
Min to Max
Point 1 65.58711 10.72824 50.74623 78.51303 35.3
Point 2 71.38729 11.89441 55.26758 83.9642 34.2
Point 3 69.63898 10.56183 54.81365 82.7013 33.7
Point 4 69.75624 10.97029 55.39466 81.04033 31.6

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120
140
160
180
200
220
240
1
2
3
4
5
6
7891011121314151617181920
1234567891011
Intensity
DistanceinPixelsY
11/10/06
12/03/07
20/04/07
Figure 4.4: Red colour intensity plot
0
10
20
30
40
50
60
70
1
2
3
4
5
6
7891011121314151617181920
1234567891011
Intensity
DistanceinPixelsY
11/10/06
12/03/07
20/04/07
Figure 4.5: Green colour intensity plot
0
5
10
15
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25
30
1
2
3
4
5
6
7891011121314151617181920
1234567891011
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DistanceinPixelsY
11/10/06
12/03/07
20/04/07
Figure 4.6: Blue colour intensity plot
Figure 4.4 illustrates
the significant
variation in red light
intensity across the
time lapse images.
This plot shows the
differences without
normalisation.
Figure 4.6 illustrates the
significant variation in
blue light intensity
across the time lapse
images. Again this plot
shows variation without
any normalisation.
Figure 4.5 illustrates
the variation in green
light intensity across
the time lapse images.
Again this plot shows
variation without any
normalisation.

33
40
50
60
70
80
90
100
110
123456789101112131415
16
17
18
19
20
2 3 4 5 6 7 8 9 10 11 12
Intensity
D
istance
in
P
ixels
Y
11/10/06
12/03/07
20/04/07
Figure 4.7: Gray-scale intensity profiles pre-normalisation
40
50
60
70
80
90
100
110
123456789101112131415
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17
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20
2 3 4 5 6 7 8 9 10 11 12
Intensity
D
istance
in
Pixels
Y
11/10/06
12/03/07
20/04/07
Figure 4.8: Gray-scale intensity profiles post-
normalisation
Figure 4.7 shows surface
plots of the vessel intensity
across 3 time lapse images.
This graph illustrates the
significant variation which
would be witnessed during
direct visual comparison of
the 3 time lapse images. This
graph represents the data
before normalisation.
Figure 4.8 shows surface plots
of the vessel intensity across 3
time lapse images. The data is
from the same images used in
Graph 6 however in this graph
the data has been normalised
in collaboration with the
calibration gray-scale included
in every image.

34
Table 4.2: Gray-scale intensity associated to vitamin C supplementation
From the 3 time lapse images a 9% change in mean intensity is witnessed between the
image taken on 12th
March and the image taken on 20th
April. This is the maximum source of
variability within these results and is similar to the 8% variation displayed across images
associated to pulse cycle.
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12
Distance in Pixels
Intensity
11th October 2006 12th March 2007 20th April 2007
Figure 4.9: Gray-scale intensity associated to vitamin C supplementation
The graph shown above in ‘Figure 4.9’ represents data taken from a central point in the
defined area of interest in each of the three time lapse images. This point was chosen to
minimise error due to image segmentation. The results are very interesting and evaluation of
these results is in the ‘Discussion’ section of this report.
Date of Image Mean Standard Deviation Minimum Maximum % Change
Min to Max
11th
Oct 2006 72.41667 10.98311 57 86 33.7
12th
Mar 2007 69.02 10.93153 54.81 80.91 32.3
20th
Apr 2007 75.9525 13.74854 57.33 91.26 37.2

35
5. Project management and GANTT charts
5.1 Project Management
• Weekly statements of progress
Statements of progress were issued to the project supervisor, via electronic mail, at the start of
each new week detailing the progress of the previous weeks work. To date there have been 23
weekly statements issued with a final statement due 7th
May 2007.
• Weekly meetings with project supervisor
Pre-arranged meetings have been held at the start of each week and every week since the
project began where discussions on the work complete and the work scheduled are common
place in order to ensure project direction and success.
• Field meetings with industry links
Numerous meetings have been held with optometrist Dr. S. Bush to knowledge share and
acquire retinal images for analysis. Dr. Bush has developed a close affiliation with this project as
the objectives of this particular project are closely related to a field of ophthalmology which Dr.
Bush is an industry expert, namely ‘CardioRetinometry®’. Meetings and contacts via e-mail,
phone, and SMS were commonplace throughout this project thus enhancing the successful
partnership of an industry expert with a leading research facility.
• Contacts made with related industries
Negotiations with TOPCON have been ongoing throughout the project, TOPCON are the
manufacturers of the equipment being used for this project and a request was issued to Mr. A.
Manichand at TOPCON via e-mail requesting the formation of a knowledge share partnership in
particular a more up to date version of TOPCON’s software ‘IMAGENET 2000’. The Overall
response from TOPCON has been very positive, however they were only able to supply the
project with a free trial version of their latest software and no conversion capabilities were built in
to the software to enable successful translation of the encrypted data files housed on the project
database.
• Acquisition of software
Numerous software packages have been acquired throughout the duration of this project to
enable more accurate analysis of the data. These include: Sigmaplot, MS Project, Matlab,
MathCAD, E Z Plot, IMAGENET 2000, COREL Photopaint, as well as the Microsoft office suite.
The majority of this software was acquired on free trial versions therefore making this project
reproducible at minimal cost.
• Seminars attended
A seminar held at the University of Hull by ‘EXTEC’ on 3 Dimensional image analysis techniques
was attended to further develop the potential for methodology used in image analysis concepts.

36
• Journal reviews
Related reports, journals, thesis and studies have been studied throughout the project to further
develop the methodology to be used to achieve the project aims and objectives. The most
relevant of these documents have been reviewed and main areas of interest have been
highlighted in the ‘Literature review’ section of this report.
• Poster presentation
A project poster presentation took place in the department of engineering, University of Hull, on
Wednesday 13th
December 2006. This offered the chance to demonstrate visually the richness of
the project and the opportunity to communicate the project to a larger audience.
• Project budget
The project budget available was £100. A small portion of the project budget has been used to
purchase 3 packs of floppy disks (10 disks per pack). These were required to retrieve data from
the retinal image database stored at Bush optometrists Hull. The actual cost of these items has
not yet been identified but is thought to be in the region of £10 thus ensuring that the project was
completed significantly under budget.
• Hours worked
To date the hours worked on this project have been in excess of 400 hours. This total includes all
time spent on activities related directly to this project.
• Milestone task completion
Initially six milestones were chosen for this project, four in semester 1 and two in semester 2. To
date 5 out of the 6 milestones have been completed on schedule with the final milestone i.e. ‘Oral
presentation’ due to take place on 9th
May 2007.
The completion dates are as follows:
- Risk identification form – 12th
October 2006 - Complete on time.
- Project Plan report – 12th
October 2006 - Complete on time.
- Project Progress report – 7th
December 2006 - Complete on time.
- Project Poster presentation – 13th
December 2006 - Complete on time.
- Project Thesis – Due 3rd
May 2007
The next milestone task to be completed this semester is:
- Project Oral presentation – Due 9th
May 2007
• GANTT Charts
The project GANTT charts have been updated on a weekly basis and a copy of the updated
version sent to the project supervisor along with the ‘weekly statement of achievement’ for each
week of the project. A revised GANTT chart was created at the start of the second semester of
this project and can be seen in the ‘GANTT Chart’ section of this report.

37
• Aims and objectives achieved
• The development of a methodology for the analysis of digital retinal images, with particular
attention to changes in the geometry and texture of blood vessels – Achieved.
• The enhancement of existing instrumentation and diagnostic techniques to facilitate the
measurement of arterial deposits via image analysis – Achieved.
• Establish a scheme for monitoring of patients general cardiovascular health through routine
non-invasive examination – Achieved.
• To investigate the potential for IP protection of the diagnostic technique and the subsequent
marketability of same to instrumentation manufacturers and/or software suppliers – Achieved.
• The future development of high value instrumentation and software to perform this time-
series image analysis – Due to the time constraints of this project this particular objective was
not successfully achieved
• To establish the feasibility of a knowledge transfer partnership - The feasibility of a
knowledge transfer partnership will be decided by the Engineering department at the
University Of Hull once this year’s project is complete.
• Unforeseen problems
During this project a number of unforeseen problems arose and subsequent re-evaluation of
techniques and methods used have to date been able to overcome these issues. One of the
more important problems encountered was the acquisition of data from the equipment being
used for this study. The equipment being used is somewhat dated in particular the software
running the PC used to collect the data from the retinal camera thus making the transfer of data
difficult. Software encryptions were in place and in order to retrieve retinal image data which
could be identified to time, date and person, annotated images had to be copied onto floppy
disks, and then these images were transferred to the project laptop via a USB hard drive.
The resolution of the camera attachment used to capture the retinal images was low due to the
age of the equipment and therefore analysis of the images was severely restricted directly due to
the quality of the images.
Another major issue was the fact that the series of time lapse images upon which the project was
due to be based came from an untrustworthy source, therefore a secondary subject (White male
30 years old) was used in order to attain a set of trustworthy images associated to regular oral
intake of vitamin C.

38
5.2 GANTT Charts
Figure 5.2.1: Final year project Semester 1:
The GANTT Chart created for semester 1 was accurate with regards to the projects
progress and aims. No modifications or revisions were required to the original GANTT chart set
out in ‘Figure 5.2.1’. The only task not to have been completed was the ‘Initial meeting with 2nd
supervisor’ as no significant problems had occurred during the first semester of the project
therefore no real requirement to hold a meeting with the 2nd
supervisor was required.
Figure 5.2.2: Final year project Semester 2 (ORIGINAL):
The GANTT Chart created for semester 2 was modified at the start of the second
semester to suit the changing requirements of the project. In particular the ‘continuation of
research’ task had been extended. Due to the vast amount of related information still to study at
the beginning of semester 2 it was felt that this research would now carry on right up to the
production of the ‘Project thesis’. Another task was also been created in the revised GANTT
Chart, namely ‘Continuation of analysis’. At the start of this project it was felt that by this stage of

39
the project the analysis of images would be complete however this was not the case due to the
expected collection of time lapse images being rejected for use. The ‘Design of automation
software/equipment’ task has been removed from the revised GANTT Chart as the
software/equipment is already in place for use, It just needs developing to suit the requirements
of this particular analysis. The ‘Development of measuring techniques’ task is also a new revision
to the semester 2 ‘revised’ GANTT Chart as research has uncovered that the only real way to
quantify changes is by direct measurement of the areas of interest.
Figure 5.2.3: Final year project Semester 2 (REVISED):
Another new task has been included in the semester 2 ‘revised’ GANTT Chart, namely
‘Project Vivas’ this task was overlooked in the ‘original’ GANTT Chart thus the reason for its
inclusion before the start of semester 2. These ‘vivas’ are meetings to be held with first & second
supervisors during weeks 13 to16 of the 2nd
semester.

40
6. Discussion
The results witnessed for the variation in vessel geometry and texture over a series of
time lapse images show effective correlation to the theoretical suggestions that; an increased
oral intake of vitamin C reduces cholesterol deposits in the retinal vasculature. On analysis of
‘Figure 4.9’, results would suggest that the central light reflex which can be seen on the 11th
October plot is seen to become less intense in the plots for 12th
March and 20th
April. This finding
is in line with the theory suggested by Dr. Bush that over a period of time and with an increased
daily intake of vitamin C the brighter deposits of cholesterol are seen to gradually disappear.
Further analysis of this graph shows that the final image taken on the 20th
April displays a plot
which has a 3.5% increase in percentage change from minimum to maximum intensity,
compared to plot for the 11th
October, this again is in line with theoretical suggestions that
increased oral intake of vitamin C increases blood flow in the retinal vessels. However the 8%
mean intensity variation discovered across the pulse related images must be taken into account
on analysis and synopsis of these results.
The results illustrated in ‘Figure 4.9’, and tabulated in ‘Table 4.2’, display a lack of
correlation with respect to the expected findings due to the fact that the plot for the 12th
March
image has a 4.7% lower overall intensity in relation to 11th
October plot. This result was not
expected as an increase in mean image intensity was expected in line with the theory. However
this result is open to further discussion as on the day of image capture the subject was displaying
signs of stress and fatigue and it was suggested that this could have an effect on the results as
initial visual study on the day suggested that the retinal vasculature had shown no signs of
improvement and possibly a reduction in health. This factor could potentially lead to a whole new
area of study as general health and well being is suggested to have an effect on cardiovascular
activity.
Due to the labour intensive nature of the analysis employed in this project, all of the data
used for analysis was interpreted and segmented by a single grader. Segmentation times can be
quite long and cause fatigue of the human observers. A second grader would have been
beneficial to the project as it would have increased segmentation precision and offered a less
dependent spread of results, unfortunately due to time and financial constraints the employment
of a second grader was not feasible.
The techniques used to analyse retinal images in this project were selected and
developed on the basis of the financial constraints of the study as well as the repeatability of this
project. This year was the first encounter with this particular project and alongside the theory, the

41
feasibility was continually being monitored. An increase in the accuracy of equipment is the only
effective way by which to accurately quantify the variation witnessed, however for the purpose of
initial research into this area of study the methodology was developed inline with the fact that this
project is economical to recreate and is achievable by non-professionals.
A major factor encountered throughout this project was the relatively low resolution of the
camera attachment used in this project. This had a significant effect on the quality of the results
obtained. It is fair to say that the higher the resolution of the camera, the more accurate the
results. As a direct result of having low resolution one of the biggest problems encountered was
the segmentation of the specific areas of interest in the images. The width of the vessel studied
was approximately 7 pixels, this meant that determining the centre of the vessel was made very
difficult as it was dependent on how the pixels were arranged across the width of the vessel in
each of the different images. Vessel wall/ boundary detection was also very difficult as it was
often mid pixel and due to this factor these edge pixels were a mixture of intensities between the
vessel and the non-vessel. Had a higher resolution camera been available the results displayed
in the graphs would have been much smoother and more accurate.
The accuracy of the variation associated to pulse cycle experiment was inconsistent due
to the procedure used to obtain images at distinctly different periods in the pulse cycle being
selected by simply manually checking for pulse variation using a thumb on wrist approach whilst
simultaneously capturing the image. Although this technique was not the most scientific the
results were still valid as they displayed a variation of 8% mean intensity across images and this
variation could be applied to the time lapse variation experiment where there was a 9% mean
intensity variation across images, therefore symbolising that potentially the variation in the time
lapse series could be associated purely to pulse cycle changes and therefore the vitamin C
supplementation could be adjudged to have had a minimal effect over the period of this study.
However if the time lapse images were all taken at exactly the same point in the pulse cycle
using a synchronised ear clip trigger device used to capture images at the desired points in the
pulse cycle then the error associated could be minimalised.
The developed methods were used to analyse retinal vessel variation across images
taken over a 6 month period of a healthy white male taking oral supplementation of vitamin C.
This study found that direct quantification of variation across images was achieved using the
models of vessel intensity profile, and that variation across images was effected by machine
accuracy, Image capture, and vessel segmentation techniques as well as physical changes in the
vasculature related to cardiac cycle.

42
Due to the time limitations involved with this project the future development of high value
instrumentation and software to perform this time-series image analysis could not be studied.
This particular area is where the future of retinal image analysis lies. The manual interpretation of
data is extremely time consuming and subject to errors associated to interpretational variation
and fatigue of graders.

43
7. Conclusions
This project has developed a methodology capable of achieving the direct quantification
of vessel geometry and texture in retinal images associated with increased oral intake of vitamin
C. Using models of vessel intensity profile presented and applied across a series of time lapse
images, results have been presented for variation in vessel geometry and texture.
The results achieved for the variation in vessel geometry and texture over a series of time
lapse images are seen to show part correlation to the theoretical suggestion which states an
increased oral intake of vitamin C reduces cholesterol deposits in the retinal vasculature.
However it should be noted that across a series of pulse related images all taken within a few
minutes of each other an 8% variation in mean intensity across the images is witnessed and if
this associated error is taken into account then the variation witnessed across the series of time
lapse images falls into this region of variation thus raising the issue of the accuracy of the results
and subsequent interpretation.
Timing the photographs to a single point in the pulse cycle will reduce variability, this
factor needs to be further investigated as the variability displayed in this study of at least 8%
across images is extremely significant when trying to identify much smaller variations due to
other factors i.e. Dietary change and vitamin supplementation.
An increase in the accuracy of equipment is the only real way to accurately quantify the
variation witnessed, however for the purpose of initial research into this area of study the
methodology was developed inline with the fact that this project is economical to recreate and is
attainable by non-professionals. In conclusion, more accurate quantification of the changes
witnessed requires the enhancement of existing instrumentation and diagnostic techniques to
facilitate the increase in accuracy in the measurement of arterial deposits via retinal image
analysis.
Researching analysis of retinal images for healthcare was found not only to be
challenging but also very rewarding. Even though it was not possible to research this topic
completely and to develop the optimised methods further due to the lack of time, this study gives
a solid foundation for further research. In the author’s opinion, any work aimed at improving
peoples quality of life is important.

44
8. Future work
The potential for future work associated to this study is vast. This project has only
effectively scraped the surface of the potential for further studies. The human body is a structure
so complicated in design that a greater understanding of the underlying mechanics must be
further evaluated to unlock the true potential of further studies related to retinal image analysis.
The time constraints involved meant that realistically only technical issues were studied. To gain
greater success in future work related to this area of study more time would be required to
investigate the biological issues concerned with similar studies.
The further development of the instrumentation used to capture retinal images is required.
As discussed in this report the quality of the images is the largest factor of in-accuracies of
results and therefore future studies will need to address this issue in order to develop the
association between cardiovascular health and retinal image analysis.
The application of more detailed clinical studies should be investigated, this study
attempted to make an association between variation in vessel geometry and texture with respect
to an increased intake of vitamin C, however this was the only parameter studied and
subsequent results show a lack of correlation. It is perceived that there are many more internal
and external parameters which could influence the results and it is postulated, for more detailed
and accurate analysis to be achieved further medical and clinical investigations into influential
conditions such as machine accuracy, grader accuracy, medical history of subjects, and the
periodic screening of subjects general health should be associated with future studies.
One area by which this study could have been further developed relatively economically
and with existing machinery would be the development of a pulse cycle measurement device
which could activate the camera at a fixed point in the pulse cycle. This device would effectively
rule out all the variation associated to cardiac cycle and therefore eliminate one of the major
sources of variation from the study of vitamin C association to cardiovascular health.

45
9. References
2.1 “Retinal image analysis: concepts, applications and potential”
American Journal of Ophthalmology, Volume 141, Issue 3, March 2006, Page 603
N. Patton, T.M. Aslam, T. MacGillivray, I.J. Deary, B. Dhillon, R.H. Eikelboom, K. Yogesan and
I.J. Constable
2.2 “Retinal image analysis using machine vision”
Department of information technology, Lappeenranta University of technology, June 6 2005.
Markku Kuivalainen.
2.3 “Measurement of vessel diameters on retinal images for cardiovascular studies”
Department of Clinical Pharmacology, Imperial College School …, 2001 - cs.bham.ac.uk
X Gao, A Bharath, A Stanton, A Hughes, N Chapman
2.4 “Characterization of changes in blood vessel width and tortuosity in retinopathy of
prematurity using image analysis”
Medical Image Analysis, Volume 6, Issue 4, December 2002, Pages 407-429
Conor Heneghan, John Flynn, Michael O’Keefe and Mark Cahill
2.5 “Are retinal arteriolar or venular diameters associated with markers for cardiovascular
disorders? The Rotterdam study”
Investigative Opthalmology & Visual Science, Volume 45, No.7, July 2004
M. Kamran Ikram, Frank Jan de Jong, Johannes R. Vingerling, Jacqueline C. M. Witteman,
Albert Hofman, Monique M. B. Breteler and Paulus T. V. M. de Jong
2.6 “Variation associated with measurement of retinal vessel diameters at different points
in the pulse cycle”
2004 - bjo.bmjjournals.com
MD Knudtson, BEK Klein, R Klein, TY Wong
2.7 “Theoretical relations between light streak characteristics and optical properties of
retinal vessels”
ACTA OPTHALMOLOGICA, 1986, VOL 179, P33-37
O. Brinchmann-Hansen and Halvor Heier.
2.8 “Retinal Microvascular Abnormalities and their Relationship with Hypertension,
Cardiovascular Disease, and Mortality”
Survey of Ophthalmology, Volume 46, Issue 1, July-August 2001, Pages 59-80
Tien Yin Wong, Ronald Klein, Barbara E. K. Klein, James M. Tielsch, Larry Hubbard and F.
Javier Nieto
2.9 “Comparative study of retinal vessel segmentation methods on a new publicly
available database”
Images Sciences Institute, Univ. Medical Centre Utrecht, Utrecht, The Netherlands.
M. Niemeijer, J. Staal, B. van Ginneken, M. Loog and M. D. Abramoff

46
10. Appendices
List of key words searched:
Analysis
Cardioretinometry
Digital
Eye
Fovea
Fundus
Image
Mydriatic Non- Mydraiatic
Ocular Oculus
Opthalmologist Opthalmology
Optometrist Optometry
Pixel
Resolution
Retina Retinal
Retinometry
RGB
Structure
Vascular
Vessel Vessels

47
The search was conducted on www.sciencedirect.com. Below are the results, the searches are
ranked by the number of hits they gained. Brackets signify a query with one of the articles i.e.
they cannot be accessed online and must be acquired through other means.
Search No. Search String No. of Hits Useful Hits
1 retinometry AND image AND analysis 0 0
2 cardioretinometry AND image AND analysis 0 0
3 fundus AND image AND analysis 2 2 (2)
4 retinal AND image AND vessel 2 1 (1)
5 retinal AND image AND analysis 4 1 (1)
6 resolution AND retinal AND images 4 1 (1)
7 retinal AND structure 75
8 digital AND image AND analysis 480
9 retinal AND images 488

48
Searches Numbers 1 & 2
Returned no results
Search Number 3
Luminosity and contrast normalization in retinal images • ARTICLE
Medical Image Analysis, Volume 9, Issue 3, June 2005, Pages 179-190
Marco Foracchia, Enrico Grisan and Alfredo Ruggeri
Computer-assisted, interactive fundus image processing for macular drusen quantitation,
• ARTICLE
Ophthalmology, Volume 106, Issue 6, 1 June 1999, Pages 1119-1125
David S. Shin, Noreen B. Javornik and Jeffrey W. Berger
Search Number 4
Quantification and characterisation of arteries in retinal images • ARTICLE
Computer Methods and Programs in Biomedicine, Volume 63, Issue 2, 1 October 2000, Pages
133-146
Xiaohong W. Gao, Anil Bharath, Alice Stanton, Alun Hughes, Neil Chapman and Simon Thom
Search Number 5
Retinal image analysis: Concepts, applications and potential • REVIEW ARTICLE
Progress in Retinal and Eye Research, Volume 25, Issue 1, January 2006, Pages 99-127
Niall Patton, Tariq M. Aslam, Thomas MacGillivray, Ian J. Deary, Baljean Dhillon, Robert H.
Eikelboom, Kanagasingam Yogesan and Ian J. Constable
Search Number 6
Ocular Higher-Order Wavefront Aberration Caused by Major Tilting Of Intraocular Lens •
SHORT COMMUNICATION
American Journal of Ophthalmology, Volume 140, Issue 4, October 2005, Pages 744-746
Tetsuro Oshika, Keisuke Kawana, Takahiro Hiraoka, Yuichi Kaji and Takahiro Kiuchi

49
Related documents already acquired:
Title: IEEE Transactions on medical imaging
Year: 2006
Article title: Segmentation of retinal blood vessels by combining the detection of centrelines and
morphological reconstruction.
Title: International journal of pattern / recognition and artificial intelligence
Year: 2005
Article title: Morphological structure reconstruction of retinal vessels in fundus images.
Related documents later acquired:
Luminosity and contrast normalization in retinal images • ARTICLE
Medical Image Analysis, Volume 9, Issue 3, June 2005, Pages 179-190
Marco Foracchia, Enrico Grisan and Alfredo Ruggeri
Computer-assisted, interactive fundus image processing for macular drusen quantitation,
• ARTICLE
Ophthalmology, Volume 106, Issue 6, 1 June 1999, Pages 1119-1125
David S. Shin, Noreen B. Javornik and Jeffrey W. Berger
Quantification and characterisation of arteries in retinal images • ARTICLE
Computer Methods and Programs in Biomedicine, Volume 63, Issue 2, 1 October 2000, Pages
133-146
Xiaohong W. Gao, Anil Bharath, Alice Stanton, Alun Hughes, Neil Chapman and Simon Thom
Retinal image analysis: Concepts, applications and potential • REVIEW ARTICLE
Progress in Retinal and Eye Research, Volume 25, Issue 1, January 2006, Pages 99-127
Niall Patton, Tariq M. Aslam, Thomas MacGillivray, Ian J. Deary, Baljean Dhillon, Robert H.
Eikelboom, Kanagasingam Yogesan and Ian J. Constable

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