Automatic and interactive retinal vessel segmentation

Retinal fundus images are used in the diagnosis and treatment of many eye conditions, such as diabetic retinopathy and glaucoma. During a clinical examination, an ophthalmologist is able to determine the onset of disease by taking certain features of the retinal vessels of the fundus into account. Often the ophthalmologist will need to select what parts of a retinal fundus image constitute vessels, so that certain statistics such as the thickness of the vessels can be calculated. Labelling all the vessels however, is a tedious and time consuming process. We make use of an edge detection approach, followed by connected component analysis and a fast Shi-Karl level-set technique, to extract the vessels from the image. Our work focuses on using image processing techniques in order to develop a computer program that can automatically and interactively detect and segment blood vessels in these images, thereby saving the ophthalmologist considerable time. We create our tool as a series of plug-ins for ImageJ, which is a public domain, Java-based image processing program developed at the National Institutes of Health. By doing so, we facilitate the use of our tool as part of a bigger system. Furthermore, ophthalmologists can easily modify the proposed vessel segmentation of our system using ImageJ, and have an ever growing library of image processing plug-ins at their disposal, to annotate, enhance and measure images.