Analysis of variation in retinal vascular assessment

Changes in retinal vascular parameters have been shown to be associated with systemic vascular diseases. The current assessment of retinal vascular parameters is based on a solo captured image and computer assisted measurement. The solo image assessment ignores the short term, dynamic change of the retinal vessel and its impact on the measurement. Variation in retinal vessel diameter during the cardiac cycle has been debated in the past, while other retinal vascular parameters have never been verified if affected by the cardiac cycle. There is a lack of comprehensive study on the various sources of variation. This thesis has comprehensively studied the variations from the various sources: (i) human cardiac cycle; (ii) multiple graders; (iii) different software; (iv) repeated photographs; (v) region of interest; (vi) the summary method and (vii) measurement protocol. The results showed there was significant change of retinal individual vessel diameters during the cardiac cycle while this change became non-significant after the individual vessel diameters were summarised using a summary method. Other retinal vascular parameters, such as tortuosity, branching angle, LDR and fractal dimension, had little to no variation over the cardiac cycle. Significant variations were found between graders and different measurement software. This thesis has shown that variation due to the cardiac cycle can be minimised using the ECG synchronised retinal photographs. The work has also suggested that the significant variations between different graders and the measurement software should be considered in all future studies when comparing their results. Number of strategies such as minimum length of measured vessel that reduce the variability in the measurement have also been identified, and these should be considered when developing new methodologies. To summarise, this thesis has identified variations and their sources in retinal vascular assessment that will contribute to reduce the variability of vessel measurements leading to improved clinical assessment and identified techniques to mitigate some of these

Automatic computation of the arteriovenous ratio and assessment of its effectiveness as a prognostic indicator in hypertension

[Resumen] La retina es la única parte del cuerpo humano en donde se pueden observar los vasos sanguíneos directamente de una forma no invasiva mediante un examen de fondo de ojo. De esta manera, la imagen de la retina mediante las técnicas de procesamiento de imágenes se convirtió en un campo de clave para el diagnóstico precoz de varias enfermedades sistémicas que provocan alteraciones visibles en dicha imagen. Así, alteraciones en el ancho de los vasos retinianos se asocian con patologías tales como diabetes o hipertensión. De hecho, el estrechamiento de las arterias constituye un indicio precoz de la hipertensión arterial sistémica, siendo una característica del grado I de la retinopatía hipertensiva de acuerdo con la clasificación de Keith-Wagener-Barker. En este sentido, se han realizado esfuerzos para desarrollar programas asistidos por ordenador para medir con precisión los cambios en el ancho de los vasos a través del índice arteriovenoso (IAV), es decir, la relación entre los calibres de las arterias y las venas. Sin embargo, aunque estos sistemas se han usado en muchos estudios con fines de investigación, su aplicabilidad en la práctica clínica diaria es todavía discutida. En este trabajo, se propone una nueva metodología para el cálculo del IAV con el fin de estratificar el riesgo cardiovascular de los hipertensos. Por un lado, se ha desarrollado un método completamente automático para estimar el IAV en una imagen de fondo de ojo de un paciente. Por otro lado, se propone un sistema para monitorizar el IAV del paciente a lo largo del tiempo. Para este fin, las mediciones del IAV en las diferentes imágenes adquiridas sobre el mismo ojo del paciente en diferentes fechas se estiman usando el mismo conjunto de vasos medidos en las mismas áreas. Por lo tanto, la mediciones obtenidos de esta manera son comparables y precisas, debido a que son independientes en el conjunto de vasos seleccionados para el cálculo. Las dos técnicas se han integrado en SIRIUS, un sistema web destinado a incluir diferentes servicios en el campo del análisis de la imagen retiniana. El sistema incluye también gestión de pacientes y revisiones, lo que facilita el análisis de las lesiones retinianas causadas por diferentes patologías y su evolución después de un determinado tratamiento. Además al ser una aplicación distribuída a través de la web, proporciona un entorno de colaboración entre diferentes médicos, investigadores y centros.[Resumo] A retina é a única parte do corpo humano onde se poden observar os vasos sanguíneos directamente dunha maneira non invasiva mediante un examen do fondo do ollo. Desta maneira, a imaxe da retina mediante as técnicas de procesamento de imáxenes converteuse nun campo chave para o diagnóstico precoz de varias enfermidades sistémicas que provocan alteracións visibles en dita imaxe. Así, cambios no ancho dos vasos retinianos asócianse con patoloxías tales como a diabetes ou a hipertensión. De feito, o estreitamento das arterias constitúe un indicio prematuro da hipertensión arterial sistémica, sendo unha característica do grado I da retinopatía hipertensiva dacordo coa clasificación de Keith- Wagener-Barker. Neste sentido, fixerónse moitos esforzos para desenvolver programas asistidos por ordenador para medir con precisión os cambios no ancho dos vasos a través do índice arteriovenoso (IAV), é dicir, a relación entre os calibres das arterias e das veas. Nembargantes, aínda que estes sistemas foron usados en moitos estudios con fins investigadores, a sua aplicabilidade na práctica clínica diaria aínda é discutida. Neste traballo, proponse unha nova metodoloxía para o cálculo do IAV co fin de estratificar o risco cardiovascular dos hipertensos. Por un lado, desenvolveuse un método completamente automático para estimar o IAV nunha imaxe de fondo de ollo dun doente. Por outra banda, proponse un sistema para monitorizar o IAV dun doente a lo longo do tempo. Para isto, as medicións do IAV nas diferentes imaxes adquiridas sobre o mesmo ollo do doente en diferentes datas fanse usando o mesmo conxunto de vasos medidos nas mesmas áreas. Polo tanto, as medicións obtidas desta maneira son comparables e precisas, debido a que son independentes do conxunto de vasos seleccionados para o cálculo. As dúas técnicas foron integradas no SIRIUS, un sistema web destinado a incluir diferentes servicios no campo da análise da imaxe retiniana. O sistema inclúe tamén xestión de doentes e revisións, facilitando a análise e estudo das lesións retinianas causadas por diferentes patoloxías e a súa evolución despois dun determinado tratamento. Ademais ao ser unha aplicación distribuída a través da web, proporciona un entorno de colaboración entre diferentes médicos, investigadores e centros.[Abstract] Retina is the only part in the human body where blood vessels can be directly observed in a non-invasive way through an eye fundus examination. In this manner, the retinal imaging assisted by image processing techniques became a key field for the early diagnosis of several systemic diseases which cause visible alterations in the fundus image. Thus, changes in the retinal vessel widths are associated with pathologies such as diabetes or hypertension. In fact, arteriolar narrowing constitutes an early sign of systemic hypertension, being a feature for the grade I of hypertension retinopathy according to Keith-Wagener-Barker classification. In this sense, some efforts have been made to develop computer-assisted programs to measure accurately abnormalities in the vessel widths through the arteriovenous ratio (AVR), that is, the relation between arteriolar and venular vessel widths. However, although these systems have been used in many studies for research purposes, their applicability to daily clinical practice is yet discussed. In this work, a new methodology for the AVR computation is proposed in order to stratify the cardiovascular risk of hypertension. On one hand, a fully automatic method to estimate the AVR in a sample patient's image is developed. On the other hand, an AVR monitoring system to compute the patient's AVR over time was implemented. To this end, the AVR measurements computed in the different patient's images acquired from the same eye at different dates, uses the same set of vessels measured at the same areas. Thus, the measurements achieved in this manner are comparable and precise due to they are independent on the set of vessels selected for the calculus. The two approaches have been integrated in SIRIUS, a web-based system aimed to include different services in the field of retinal image analysis. It includes patient and checkup management, making easier to analyze the retinal lesions caused by different pathologies and their evolution after a specific treatment. Moreover, being a application distributed via the web, it provides a collaborative environment among different physicians, researchers and medical centers

Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, INSPIRE, IOSTAR, VICAVR, DRIVE and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community

Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement

The relationship between changes in retinal vessel morphology and the onset and progression of diseases such as diabetes, hypertension and retinopathy of prematurity (ROP) has been the subject of several large scale clinical studies. However, the difficulty of quantifying changes in retinal vessels in a sufficiently fast, accurate and repeatable manner has restricted the application of the insights gleaned from these studies to clinical practice. This paper presents a novel algorithm for the efficient detection and measurement of retinal vessels, which is general enough that it can be applied to both low and high resolution fundus photographs and fluorescein angiograms upon the adjustment of only a few intuitive parameters. Firstly, we describe the simple vessel segmentation strategy, formulated in the language of wavelets, that is used for fast vessel detection. When validated using a publicly available database of retinal images, this segmentation achieves a true positive rate of 70.27%, false positive rate of 2.83%, and accuracy score of 0.9371. Vessel edges are then more precisely localised using image profiles computed perpendicularly across a spline fit of each detected vessel centreline, so that both local and global changes in vessel diameter can be readily quantified. Using a second image database, we show that the diameters output by our algorithm display good agreement with the manual measurements made by three independent observers. We conclude that the improved speed and generality offered by our algorithm are achieved without sacrificing accuracy. The algorithm is implemented in MATLAB along with a graphical user interface, and we have made the source code freely available

Discovery of retinal biomarkers for vascular conditions through advancement of artery-vein detection and fractal analysis

Research into automatic retina image analysis has become increasingly important, not just in ophthalmology but also in other clinical specialities such as cardiology and neurology. In the retina, blood vessels can be directly visualised non-invasively in-vivo, and hence it serves as a "window" to cardiovascular and neurovascular complications. Biomarker research, i.e. investigating associations between the morphology of the retinal vasculature (as a means of revealing microvascular health or disease) and particular conditions affecting the body or brain could play an important role in detecting disease early enough to impact on patient treatment and care. A fundamental requirement of biomarker research is access to large datasets to achieve sufficient power and significance when ascertaining associations between retinal measures and clinical characterisation of disease. Crucially, the vascular changes that appear can affect arteries and veins differently. An essential part of automatic systems for retinal morphology quantification and biomarker extraction is, therefore, a computational method for classifying vessels into arteries and veins. Artery-vein classification enables the efficient extraction of biomarkers such as the Arteriolar to Venular Ratio, which is a well-established predictor of stroke and other cardiovascular events. While structural parameters of the retinal vasculature such as vessels calibre, branching angle, and tortuosity may individually convey some information regarding specific aspects of the health of the retinal vascular network, they do not convey a global summary of the branching pattern and its state or condition. The retinal vascular tree can be considered a fractal structure as it has a branching pattern that exhibits the property of self-similarity. Fractal analysis, therefore, provides an additional means for the quantitative study of changes to the retinal vascular network and may be of use in detecting abnormalities related to retinopathy and systemic diseases. In this thesis, new developments to fully automated retinal vessel classification and fractal analysis were explored in order to extract potential biomarkers. These novel processes were tested and validated on several datasets of retinal images acquired with fundus cameras. The major contributions of this thesis include: 1) developing a fully automated retinal blood vessel classification technique, 2) developing a fractal analysis technique that quantifies regional as well as global branching complexity, 3) validating the methods using multiple datasets, and 4) applying the proposed methods in multiple retinal vasculature analysis studies