Robust retinal vessel segmentation via clustering-based patch mapping functions

Robust vessel segmentation of fundus images is of great interest for better diagnosis of many diseases like diabetic retinopathy, retinopathy of prematurity, vein occlusions and so on. In this paper, we propose a novel example-based vessel segmentation method, based on learning the mapping relationship between fundus images and their corresponding ground truths. Firstly, the training images and their corresponding ground truths are divided into patches and clustered. Secondly, the mapping functions for each cluster are computed in a simple and efficient way from the training patches to their manual segmentation patches. Finally, Vessel segmentation are reconstructed by the simple mapping functions. Experimental results show that our method is efficient and can achieve competitive performance for vessel segmentation problems.