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

Structure analysis and lesion detection from retinal fundus images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.Mexican National Council of Science and Technolog

Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy