Detection of hemorrhage and exudates in retinal fundus image of diabetic patients

Diabetes is a disease that interferes with the body's ability to use and store sugar, which can cause many health problems. Over time, diabetes affects the circulatory system including the retina. As diabetes progress, the vision of a patient may start to deteriorate and then leading to Diabetic Retinopathy (DR) which further will cause blindness. So, early detection of the disease is important to avoid blindness. There are several ways to diagnose DR and slit – lamp examination is one of the traditional method used by the ophthalmologist. This method requires the clinician to see directly into patient’s eye through an ophthalmoscope or the slit lamp machine to determine whether or not the eyes contain any abnormal features that indicate DR. However, this is not the most effective method yet. Any human can get tired and drowsy including doctors. This natural flaws of human being can affect the diagnosis and then causing false result analysis. Besides, every individuals doesn’t hold same opinion and judgment. Therefore, this project is proposed to assist the clinicians in identifying DR. There are two main abnormal features that are formed in the retina of a diabetic retinopathy’s patient. They are hemorrhage and exudates. Hemorrhage are formed as a result due to leakage of retinal blood vessel which has similar red colour to the vessel. Whereas exudates are yellow-white deposits structure on the retina that is formed due to leakage of blood from abnormal vessels. This thesis mainly focuses on developing a Fundus Image Analysis (FIA) system that extracts the anatomical and both the abnormal features of the retina in order to diagnose the disease. This research is carried out in three phases. In the first phase, an automated system is developed to distinguish the anatomical features of the retina from the abnormal features. This phase is called the Masking Phase. This phase involved combinations of several image processing techniques including Specify Polygonal Region of Interest (ROIPOLY), Contrast-limited adaptive histogram equalization (CLAHE), Morphological Opening and Structuring, Median Filtering and Thresholding. The second phase is the Haemorrhage Extraction phase. In this phase, Saturation Adjust Method, Morphological operations and Regional Minima technique is proposed. The third and the last phase is the Exudates Extraction phase. In this phase, Edge Detection, Gradient Magnitude and Region Of Interest techniques are combined to form a complete working algorithm. The experimented images in this project are the retinal fundus images that was taken from a public database (diaretdb1 - Standard Diabetic Retinopathy Database). It is a public database for benchmarking diabetic retinopathy detection from digital images. By using this database and the defined testing protocol, the results between different methods can be compared. At the end of this project, the result shows that the method applied is able to detect exudates features and capable of detecting and distinguishing hemorrhage from blood vessels. Final result shows the accuracy of 48.3% for detecting images with haemorrhages and 68.5% for images with exudates

Automatic detection of microaneurysms in RGB retinal fundus images

In this study, an efficient and fast-working method to detect microaneurysm lesions, first symptom of diabetic retinopathy, is described. The proposed method is based on mathematical morphology, object pixel classification and connected component analysis. The proposed algorithm responses in 4.8 seconds for 2048x1536 pixel images. This shows this system runs faster than other microaneurysm detection systems. The sensitivity and specificity of this system is 69.1% and 99.3% specificity, respectively

Detección Automática de Microaneurismas en Retinografías para Diagnóstico Precoz de Retinopatía Diabética

En este trabajo presentamos un prototipo de herramienta de detección automática de microaneurismas (MA) en retinografías en color. Este algoritmo evoluciona a partir de trabajos anteriores como la detección de microcalcificaciones en mamografías [1] o la detección de MA en angiografías fluoresceínicas (AF) [2][3]. El método para la detección automática de MA se divide en cinco partes: preprocesado de la retinografía, algoritmo de detección basado en la umbralización del error de predicción lineal en 2D, crecimiento de regiones, selección de características, y clasificación de los candidatos mediante una red neuronal del tipo Fuzzy ARTMAP. En total disponemos de 30 imágenes con 421 MA diagnosticados, de los cuales 101 se han utilizado para la clasificación. El algoritmo detecta correctamente 78 MA, presentando una sensibilidad del 77.23% y una media de 19.25 falsos positivos por imagen.Ministerio de Sanidad PI07/90379Ministerio de Sanidad PI07/9037

Detección Automática de Microaneurismas en Retinografías

La detección de microaneurismas (MA) en retinografías es esencial a la hora de realizar un diagnóstico precoz de la retinopatía diabética (RD). Con esta finalidad, se presenta una herramienta automática que, tras preprocesar la retinografía con métodos basados en intensidad y tras realizar una selección de semillas mediante una umbralización adaptativa, obtiene una serie de candidatos a MA mediante un proceso de crecimiento de regiones, a partir de las cuales se seleccionan los verdaderos MA mediante una clasificación con una red neuronal Fuzzy ARTMAP. La evaluación del algoritmo se ha realizado con una base de datos consistente en 53 retinografías con 256 MA marcados por un oftalmólogo experto. 42 (204 MA) de estas imágenes han sido empleadas para entrenar al clasificador sirviendo las 11 (52 MA) restantes para la fase de prueba. La presente propuesta obtiene una sensibilidad (S) de 78.85% y una media de 9 falsos positivos por imagen (FPpI)

Computerized Approaches for Retinal Microaneurysm Detection

The number of diabetic patients throughout the world is increasing with a very high rate. The patients suffering from long term diabetes have a very high risk of generating retinal disorder called Diabetic Retinopathy(DR). The disease is a complication of diabetes and may results in irreversible blindness to the patient. Early diagnosis and routine checkups by expert ophthalmologist possibly prevent the vision loss. But the number of people to be screen exceeds the number of experts, especially in rural areas. Thus the computerized screening systems are needed which will accurately screen the large amount of population and identify healthy and diseased people. Thus the workload on experts is reduced significantly. Microaneurysms(MA) are first recognizable signs of DR. Thus early detection of DR requires accurate detection of Microaneurysms. Computerized diagnosis insures reliable and accurate detection of MA's. The paper overviews the approaches for computerized detection of retinal Microaneurysms

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method