Computer-aided diagnosis system for the assessment of retinal vascular changes

This paper presents an automatic application that provides several retinal image analysis functionalities, namely vessel segmentation, vessel width estimation, artery/vein classification and optic disc segmentation. A pipeline of these methods allows the computation of important vessel related indexes, namely the Central Retinal Arteriolar Equivalent (CRAE), Central Retinal Venular Equivalent (CRVE) and Arteriolar-to-Venular Ratio (AVR), as well as various geometrical features associated with vessel bifurcations. The results for AVR estimation were assessed using the images of INSPIRE-AVR dataset; for this dataset, the mean error of the measured AVR values with respect to the reference was identical to the one achieved by a medical expert. The estimation of the CRAE, CRVE and AVR values on 480 images from 120 subjects have shown a significant correlation between right and left eyes and also between images of same eye acquired with different camera fields of view

RetinaCAD - retinal computer-aided diagnosis system

This paper presents an automatic application that provides several retinalimage analysis functionalities, namely vessel segmentation, vesselwidth estimation, artery/vein classification and optic disc segmentation. Apipeline of these methods allows the computation of important vessel relatedindexes, namely the Central Retinal Arteriolar Equivalent (CRAE),Central Retinal Venular Equivalent (CRVE) and Arteriolar-to-Venular Ratio(AVR), as well as various geometrical features associated with vesselbifurcation

Patents in the computer-aided diagnosis industry

Computer aided diagnosis is a relatively new field, through the use of new techniques algorithms and technologies, it can help technicians perform a better and faster analysis, reduce or even substitute part of their workload. Patents are windows into a company's technological assets, as well as into the state of a certain technology field. In this thesis we analyzed patents that are mainly related to the automated analysis of human retinopathies. Using patent search engines we explored the various patent databases, using keywords related to the area and the international patent classification to refine the search and eliminate unrelated results, proceeding then to a thorough analysis of the dataset. By analyzing the structured and unstructured text, contained in the obtained patents, different observations where made: major players in the field,patent timelines, main technologies involved and the direction of the technology evolution

A review of feature-based retinal image analysis

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Computer analysis for registration and change detection of retinal images

The current system of retinal screening is manual; It requires repetitive examination of a large number of retinal images by professional optometrists who try to identify the presence of abnormalities. As a result of the manual and repetitive nature of such examination, there is a possibility for error in diagnosis, in particular in the case when the progression of disease is slight. As the sight is an extremely important sense, any tools which can improve the probability of detecting disease could be considered beneficial. Moreover, the early detection of ophthalmic anomalies can prevent the impairment or loss of vision. The study reported in this Thesis investigates computer vision and image processing techniques to analyse retinal images automatically, in particular for diabetic retinopathy disease which causes blindness. This analysis aims to automate registration to detect differences between a pair of images taken at different times. These differences could be the result of disease progression or, occasionally, simply the presence of artefacts. The resulting methods from this study, will be therefore used to build a software tool to aid the diagnosis process undertaken by ophthalmologists. The research also presents a number of algorithms for the enhancement and visualisation of information present within the retinal images, which under normal situations would be invisible to the viewer; For instance, in the case of slight disease progression or in the case of similar levels of contrast between images, making it difficult for the human eye to see or to distinguish any variations. This study also presents a number of developed methods for computer analysis of retinal images. These methods include a colour distance measurement algorithm, detection of bifurcations and their cross points in retina, image registration, and change detection. The overall analysis in this study can be classified to four stages: image enhancement, landmarks detection, registration, and change detection. The study has showed that the methods developed can achieve automatic, efficient, accurate, and robust implementation

Retinal Vascular Measurement Tools for Diagnostic Feature Extraction

The contributions of this work are in the development of new and state of the art algorithms for retinal image analysis including optic disc detection, tortuosity estimation, and cross-over abnormality detection. The retina is one of the only areas of the human body that blood vessels can be visualized noninvasively. Retinal imaging has become a standard in the ophthalmologist’s office because it is an easy and inexpensive way to monitor not just eye health, but also systemic vascular diseases. Changes to the retinal vasculature can be the early signs of diseases such as diabetic and hypertensive retinopathy, of which early detection can save vision, money, and improve overall health for the patient. When looking at the retinal vasculature, ophthalmologists generally rely on a qualitative assessment which can make comparisons over time or between different ophthalmologists difficult. Computer aided systems are now able to quantify what the ophthalmologist is qualitatively measuring in what they consider to be the most important features of the vasculature. These include, but are not limited to, tortuosity, arteriolar narrowing, cross-over abnormalities, and artery-vein (AV) ratio. The University of Padova has created a semi-automatic system for detecting and quantifying retinal vessels starting from optic disc detection, vessel segmentation, width estimation, tortuosity calculation, AV classification, and AV ratio. We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The second contribution is a new algorithm for calculating abnormalities at AV crossing points. In retinal images, Gunn’s sign appears as a tapering of the vein at a crossing point, while Salus’s sign presents as an S-shaped curving. This work presents a method for the automatic quantification of these two signs once a crossover has been detected; combining segmentation, artery vein classification, and morphological feature extraction techniques to calculate vein widths and angles entering and exiting the crossover. Results on two datasets show separation between the two classes and that we can reliably detect and quantify these signs under the right conditions. The last contribution in tortuosity consists of two parts. A comparative study was performed on several of the most popular methods for tortuosity estimation on a new vessel dataset. Results show that several methods have good Cohen’s kappa agreement with both graders, while the tortuosity density metric has the highest single metric average agreement across vessel type and grader. The second is a new way to enhance curvature in segmented vessels based on a difference of Gabor filters to create a curvature enhanced image. The proposed method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. This held true after testing the method on the propose dataset as well, having higher correlation values across grader and vessel type compared with other tortuosity metrics. Summary of Results: The optic disc detection method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The AV nicking quantification method was tested on a small dataset of 10 crossing provided by doctors at Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece. Results showed separation between the normal and abnormal classes for both the Gunn and Salus sign. The method was then tested on a larger, publicly available dataset which showed good separation for the Gunn sign. The proposed tortuosity method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. It was then tested on the dataset proposed in this thesis, further corroborating the effectiveness of the method

Automatic analysis of retinal images to aid in the diagnosis and grading of diabetic retinopathy

Diabetic retinopathy (DR) is the most common complication of diabetes mellitus and one of the leading causes of preventable blindness in the adult working population. Visual loss can be prevented from the early stages of DR, when the treatments are effective. Therefore, early diagnosis is paramount. However, DR may be clinically asymptomatic until the advanced stage, when vision is already affected and treatment may become difficult. For this reason, diabetic patients should undergo regular eye examinations through screening programs. Traditionally, DR screening programs are run by trained specialists through visual inspection of the retinal images. However, this manual analysis is time consuming and expensive. With the increasing incidence of diabetes and the limited number of clinicians and sanitary resources, the early detection of DR becomes non-viable. For this reason, computed-aided diagnosis (CAD) systems are required to assist specialists for a fast, reliable diagnosis, allowing to reduce the workload and the associated costs. We hypothesize that the application of novel, automatic algorithms for fundus image analysis could contribute to the early diagnosis of DR. Consequently, the main objective of the present Doctoral Thesis is to study, design and develop novel methods based on the automatic analysis of fundus images to aid in the screening, diagnosis, and treatment of DR. In order to achieve the main goal, we built a private database and used five retinal public databases: DRIMDB, DIARETDB1, DRIVE, Messidor and Kaggle. The stages of fundus image processing covered in this Thesis are: retinal image quality assessment (RIQA), the location of the optic disc (OD) and the fovea, the segmentation of RLs and EXs, and the DR severity grading. RIQA was studied with two different approaches. The first approach was based on the combination of novel, global features. Results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity using the private database. We developed a second approach aimed at RIQA based on deep learning. We achieved 95.29% accuracy with the private database and 99.48% accuracy with the DRIMDB database. The location of the OD and the fovea was performed using a combination of saliency maps. The proposed methods were evaluated over the private database and the public databases DRIVE, DIARETDB1 and Messidor. For the OD, we achieved 100% accuracy for all databases except Messidor (99.50%). As for the fovea location, we also reached 100% accuracy for all databases except Messidor (99.67%). The joint segmentation of RLs and EXs was accomplished by decomposing the fundus image into layers. Results were computed per pixel and per image. Using the private database, 88.34% per-image accuracy (ACCi) was reached for the RL detection and 95.41% ACCi for EX detection. An additional method was proposed for the segmentation of RLs based on superpixels. Evaluating this method with the private database, we obtained 84.45% ACCi. Results were validated using the DIARETDB1 database. Finally, we proposed a deep learning framework for the automatic DR severity grading. The method was based on a novel attention mechanism which performs a separate attention of the dark and the bright structures of the retina. The Kaggle DR detection dataset was used for development and validation. The International Clinical DR Scale was considered, which is made up of 5 DR severity levels. Classification results for all classes achieved 83.70% accuracy and a Quadratic Weighted Kappa of 0.78. The methods proposed in this Doctoral Thesis form a complete, automatic DR screening system, contributing to aid in the early detection of DR. In this way, diabetic patients could receive better attention for their ocular health avoiding vision loss. In addition, the workload of specialists could be relieved while healthcare costs are reduced.La retinopatía diabética (RD) es la complicación más común de la diabetes mellitus y una de las principales causas de ceguera prevenible en la población activa adulta. El diagnóstico precoz es primordial para prevenir la pérdida visual. Sin embargo, la RD es clínicamente asintomática hasta etapas avanzadas, cuando la visión ya está afectada. Por eso, los pacientes diabéticos deben someterse a exámenes oftalmológicos periódicos a través de programas de cribado. Tradicionalmente, estos programas están a cargo de especialistas y se basan de la inspección visual de retinografías. Sin embargo, este análisis manual requiere mucho tiempo y es costoso. Con la creciente incidencia de la diabetes y la escasez de recursos sanitarios, la detección precoz de la RD se hace inviable. Por esta razón, se necesitan sistemas de diagnóstico asistido por ordenador (CAD) que ayuden a los especialistas a realizar un diagnóstico rápido y fiable, que permita reducir la carga de trabajo y los costes asociados. El objetivo principal de la presente Tesis Doctoral es estudiar, diseñar y desarrollar nuevos métodos basados en el análisis automático de retinografías para ayudar en el cribado, diagnóstico y tratamiento de la RD. Las etapas estudiadas fueron: la evaluación de la calidad de la imagen retiniana (RIQA), la localización del disco óptico (OD) y la fóvea, la segmentación de RL y EX y la graduación de la severidad de la RD. RIQA se estudió con dos enfoques diferentes. El primer enfoque se basó en la combinación de características globales. Los resultados lograron una precisión del 91,46% utilizando la base de datos privada. El segundo enfoque se basó en aprendizaje profundo. Logramos un 95,29% de precisión con la base de datos privada y un 99,48% con la base de datos DRIMDB. La localización del OD y la fóvea se realizó mediante una combinación de mapas de saliencia. Los métodos propuestos fueron evaluados sobre la base de datos privada y las bases de datos públicas DRIVE, DIARETDB1 y Messidor. Para el OD, logramos una precisión del 100% para todas las bases de datos excepto Messidor (99,50%). En cuanto a la ubicación de la fóvea, también alcanzamos un 100% de precisión para todas las bases de datos excepto Messidor (99,67%). La segmentación conjunta de RL y EX se logró descomponiendo la imagen del fondo de ojo en capas. Utilizando la base de datos privada, se alcanzó un 88,34% de precisión por imagen (ACCi) para la detección de RL y un 95,41% de ACCi para la detección de EX. Se propuso un método adicional para la segmentación de RL basado en superpíxeles. Evaluando este método con la base de datos privada, obtuvimos 84.45% ACCi. Los resultados se validaron utilizando la base de datos DIARETDB1. Finalmente, propusimos un método de aprendizaje profundo para la graduación automática de la gravedad de la DR. El método se basó en un mecanismo de atención. Se utilizó la base de datos Kaggle y la Escala Clínica Internacional de RD (5 niveles de severidad). Los resultados de clasificación para todas las clases alcanzaron una precisión del 83,70% y un Kappa ponderado cuadrático de 0,78. Los métodos propuestos en esta Tesis Doctoral forman un sistema completo y automático de cribado de RD, contribuyendo a ayudar en la detección precoz de la RD. De esta forma, los pacientes diabéticos podrían recibir una mejor atención para su salud ocular evitando la pérdida de visión. Además, se podría aliviar la carga de trabajo de los especialistas al mismo tiempo que se reducen los costes sanitarios.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione