Automatic detection of neovascularization in retinal images using extreme learning machine

Diabetic Retinopathy is one complication of diabetes, which can cause blindness. Diabetic retinopathy can be divided into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopahy (PDR), and neovascularization is a key symbol to make diagnosis between them. An automatic detection of neovascularization in retinal images using extreme learning machine is proposed. Furthermore, we use a series of filter banks to get the features of neovascularization from retinal images. The detection framework is evaluated with images annotated by expert ophthalmologists based on the images selected from several public retinal image databases. The experimental results illustrate that the framework can mark and show the suspected neovascularization regions to ophthalmologists, and thus support for their decision making.