An Improved Retinal Vessel Segmentation Method Based on Supervised Learning

This paper propose an improved supervised method for retinal vessel segmentation based on Extreme Learning Machine (ELM). Firstly, a 36-D feature vector is extracted for each pixel of the fund us image consisting of local features, morphological features and divergence of vector fields. Then a matrix for pixels of the training set using the feature vector and the manual segmentation is constructed as the input of the ELM. Finally a classifier is obtained to segment the retinal vessels. The method is evaluated with the DRIVE database and the average accuracy is 0.9581. And the running time is greatly decreased by using ELM. It is applicable for computer-aided diagnosis and disease screening.