Department of Engineering. University of Hull. Final Year Project 2006/2007 ANALYSIS OF RETINAL IMAGES FOR HEALTHCARE

Transcription

1 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 3 rd May 2007

2 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

3 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

4 Contents Page No. 1 Introduction Objectives and limitations Structure of the thesis 11 2 Review of previous work 12 3 Description of methods and procedures Image capture 24 Figure Sample retinal image Image processing Image segmentation 25 Figure Area of interest Image analysis Data analysis 26 Figure Position of datum on vessel 26 Figure Vessel area of interest 27 Figure Dimensional plot of vessel intensity fit 28 Figure 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 Project management GANTT Charts 38 Figure Semester 1 GANTT Chart 38 Figure Semester 2 GANTT Chart 38 Figure Semester 2 Revised GANTT Chart 39 4

5 6 Discussion 40 7 Conclusions 43 8 Future work 44 9 References Appendices Publicity page 50 5

6 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

7 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

8 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 highresolution 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

9 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

10 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 noninvasive 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

11 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. 11

12 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 12

13 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

14 2.2 Retinal image analysis using machine vision Department of information technology, Lappeenranta University of technology, June 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

15 2.3 Measurement of vessel diameters on retinal images for cardiovascular studies Department of Clinical Pharmacology, Imperial College School, 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

16 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 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

17 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

18 2.6 Variation associated with measurement of retinal vessel diameters at different points in the pulse cycle 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

19 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 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

20 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 = 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

21 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 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

22 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

23 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 regiongrowing 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

24 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 : 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 11 th October 2006, and then a further set on the 12 th March 2007, followed by a final set taken on the 20 th 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

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 25

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 x 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 Figure 3.5.1: Position of datum in vessel 26

27 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 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 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

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 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

29 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 The gathered data from the area of interest displayed in Figure can then be statistically analysed to offer a true reflection of the variation across time lapse images. (See Results section) 29

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. 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 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. Intensity Distance in Pixels Y Distance in Pixels X Point 1 Point 2 Point 3 Point 4 Figure 4.2: Gray-scale intensity plot post-normalisation 30

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 Pulse point Mean Standard Deviation Minimum Maximum % Change Min to Max Point Point Point Point 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 Intensity Distance in Pixels Figure 4.3: Gray-scale intensity profile with variation due to pulse cycle 31

32 Intensity Intensity Intensity /10/06 12/03/07 20/04/ Distance in Pixels X 4 3 Distance in Pixels Y Figure 4.4 illustrates the significant variation in red light intensity across the time lapse images. This plot shows the differences without normalisation. Figure 4.4: Red colour intensity plot /10/06 12/03/07 20/04/07 Figure 4.5 illustrates the variation in green light intensity across the time lapse images. Again this plot shows variation without any normalisation Distance in Pixels X 4 3 Distance in Pixels Y Figure 4.5: Green colour intensity plot /10/06 12/03/07 20/04/ Distance in Pixels X Distance in Pixels Y 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 4.6: Blue colour intensity plot 32

33 Intensity Intensity /10/06 12/03/07 20/04/07 Distance in Pixels Y 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 Distance in Pixels X Figure 4.7: Gray-scale intensity profiles pre-normalisation 110 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 /10/06 12/03/07 20/04/07 Distance in Pixels Y Distance in Pixels X Figure 4.8: Gray-scale intensity profiles postnormalisation 33

34 Date of Image Mean Standard Deviation Minimum Maximum % Change Min to Max 11 th Oct th Mar th Apr 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 12 th March and the image taken on 20 th 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 th October th March th April Intensity Distance in Pixels 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. 34

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 7 th May 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 , 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 requesting the formation of a knowledge share partnership in particular a more up to date version of TOPCON s software IMAGENET 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