Department of Engineering
This presentation is the property of its rightful owner.
Sponsored Links
1 / 50

Department of engineering hull uni PowerPoint PPT Presentation


Know about Department of engineering hull uni, for more info call at 07932 162223.http://www.lifeextensionoptometry.com/

Download Presentation

Department of engineering hull uni

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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


Department of engineering hull uni

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.

2


Department of engineering hull uni

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.

3


Department of engineering hull uni

Contents

Page No.

1 Introduction

10

1.1

Objectives and limitations

10

2 Review of previous work

1.2

Structure of the thesis

11

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

4 Results

Figure 3.5.4

Calibration scale for gray-scale intensity

29

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

Gray-scale intensity associated to vit-C supplementation

5 Project management summary

Figure 4.9

34

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

4


Department of engineering hull uni

6 Discussion

40

7 Conclusions

43

8 Future work

44

9 References

45

10 Appendices

46

11 Publicity page

50

5


Department of engineering hull uni

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.

6


Department of engineering hull uni

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.

7


Department of engineering hull uni

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.

8


Department of engineering hull uni

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.

9


Department of engineering hull uni

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.

10


Department of engineering hull uni

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.

Structure of the thesis

1.2

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.

11


Department of engineering hull uni

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:

Retinal image analysis: Concepts, applications and potential

2.1

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

Retinal micro vascular abnormalities and their relationship with hypertension,

2.8

cardiovascular disease and mortality

2.9

Comparative study of retinal vessel segmentation methods on a new publicly available

database

12


Department of engineering hull uni

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”.

13


Department of engineering hull uni

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.

14


Department of engineering hull uni

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.

15


Department of engineering hull uni

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.

16


Department of engineering hull uni

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.

17


Department of engineering hull uni

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)

18


Department of engineering hull uni

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.

19


Department of engineering hull uni

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

20


Department of engineering hull uni

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.

21


Department of engineering hull uni

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”.

22


Department of engineering hull uni

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.

23


Department of engineering hull uni

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.

24


Department of engineering hull uni

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

25


Department of engineering hull uni

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

26


Department of engineering hull uni

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’.

27


Department of engineering hull uni

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

28


Department of engineering hull uni

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)

29


Department of engineering hull uni

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.

100

Figure 4.1 illustrates a

surface plot of the 4 different

90

points in the pulse cycle

across the vessel which is

80

being studied. The variation

Intensity

70

between each of the plots is

Point 1

Point 2

Point 3

Point 4

quite significant and this is

60

Distance in Pixels Y

what is witnessed when

1234567891011121314151617181920

10

11

simply comparing images on

50

a PC monitor, hence leading

40

2

3

4

5

6

7

8

9

12

to visual differences being

Distance in Pixels X

recognised.

Figure 4.1: Gray-scale intensity plot pre-normalisation

Figure 4.2 illustrates how the

100

variation decreases in surface

plots of the 4 different points in

90

the pulse cycle across the vessel

which is being studied. This

80

graph displays the same 4 points

Intensity

70

in the pulse cycle as Figure 4.1

Point 1

Point 2

Point 3

Point 4

however in this graph the data

60

Distance in Pixels Y

1234567891011121314151617181920

10

11

has been normalised in

50

collaboration with the calibration

gray-scale included in every

40

2

3

4

5

6

7

8

9

12

image.

Distance in Pixels X

Figure 4.2: Gray-scale intensity plot post-normalisation

30


Department of engineering hull uni

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

Pulse point

Mean

Standard Deviation

Minimum

Maximum % Change

Min to Max

65.58711

10.72824

50.74623

78.51303

Point 1

35.3

71.38729

11.89441

55.26758

83.9642

Point 2

34.2

69.63898

10.56183

54.81365

82.7013

Point 3

33.7

69.75624

10.97029

55.39466

81.04033

Point 4

31.6

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.

Point 1

Point 2

Point 3

Point 4

90

80

70

60

Intensity

50

40

30

20

10

0

1

2

3

4

5

6

7

8

9

10

11

12

Distance in Pixels

Figure 4.3: Gray-scale intensity profile with variation due to pulse cycle

31


Department of engineering hull uni

240

11/10/06

12/03/07

20/04/07

220

Figure 4.4 illustrates

the significant

variation in red light

intensity across the

time lapse images.

This plot shows the

differences without

normalisation.

200

Intensity

180

160

Distance in Pixels Y

20

18

19

16

17

14

15

12

13

140

11

10

9

8

7

6

5

4

3

2

120

11

1

10

9

8

7

6

5

4

3

2

1

Distance in Pixels X

Figure 4.4: Red colour intensity plot

70

11/10/06

12/03/07

20/04/07

60

Figure 4.5 illustrates

the variation in green

light intensity across

the time lapse images.

Again this plot shows

variation without any

normalisation.

50

40

Intensity

30

Distance in Pixels Y

20

20

18

19

16

17

14

15

12

13

10

11

10

9

8

7

6

5

4

3

2

0

11

1

10

9

8

7

6

5

4

3

2

1

Distance in Pixels X

Figure 4.5: Green colour intensity plot

30

11/10/06

12/03/07

20/04/07

25

Figure 4.6 illustrates the

significant variation in

blue light intensity

across the time lapse

images. Again this plot

shows variation without

any normalisation.

20

Intensity

15

10

Distance in Pixels Y

19

20

17

18

15

16

13

14

11

12

5

9

10

8

7

6

5

4

3

2

0

11

1

10

9

8

7

6

5

4

3

2

1

Distance in Pixels X

Figure 4.6: Blue colour intensity plot

32


Department of engineering hull uni

110

Figure 4.7 shows surface

11/10/06

12/03/07

20/04/07

100

plots of the vessel intensity

across 3 time lapse images.

90

This graph illustrates the

80

significant variation which

Intensity

would be witnessed during

70

direct visual comparison of

Distance in Pixels Y

the 3 time lapse images. This

60

1234567891011121314151617181920

10

11

graph represents the data

50

before normalisation.

40

2

3

4

5

6

7

8

9

12

Distance in Pixels X

Figure 4.7: Gray-scale intensity profiles pre-normalisation

110

11/10/06

12/03/07

20/04/07

100

Figure 4.8 shows surface plots

of the vessel intensity across 3

90

time lapse images. The data is

from the same images used in

80

Intensity

Graph 6 however in this graph

70

the data has been normalised

in collaboration with the

60

Distance in Pixels Y

calibration gray-scale included

1234567891011121314151617181920

10

11

in every image.

50

40

2

3

4

5

6

7

8

9

12

Distance in Pixels X

Figure 4.8: Gray-scale intensity profiles post-

normalisation

33


Department of engineering hull uni

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

Table 4.2: Gray-scale intensity associated to vitamin C supplementation

image taken on 12th March and the image taken on 20th April. This is the maximum source of

From the 3 time lapse images a 9% change in mean intensity is witnessed between the

variability within these results and is similar to the 8% variation displayed across images

associated to pulse cycle.

11th October 2006

12th March 2007

20th April 2007

100

90

80

70

60

Intensity

50

40

30

20

10

0

1

2

3

4

5

6

7

8

9

10

11

12

Distance in Pixels

Figure 4.9: Gray-scale intensity associated to vitamin C supplementation

defined area of interest in each of the three time lapse images. This point was chosen to

The graph shown above in ‘Figure 4.9’ represents data taken from a central point in the

minimise error due to image segmentation. The results are very interesting and evaluation of

these results is in the ‘Discussion’ section of this report.

34


Department of engineering hull uni

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.

35


Department of engineering hull uni

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.

36


Department of engineering hull uni

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.

37


Department of engineering hull uni

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

38


Department of engineering hull uni

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.

39


Department of engineering hull uni

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

40


Department of engineering hull uni

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.

41


Department of engineering hull uni

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.

42


Department of engineering hull uni

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.

43


Department of engineering hull uni

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.

44


Department of engineering hull uni

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

45


Department of engineering hull uni

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

46


Department of engineering hull uni

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

47


Department of engineering hull uni

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

48


Department of engineering hull uni

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

49


Department of engineering hull uni

Publicity page

50


  • Login