Mapping Functions Driven Robust Retinal Vessel Segmentation via Training Patches

Vein occlusions and diabetic retinopathy are two of many retinal pathologies affecting the retina. Understanding robust vessel segmentation of fundus images is of vital importance for improving the diagnosis results of these diseases. This paper proposes a novel approach for computing the minimum distance for each test patch via the distance comparison within the test patch and cluster centers. The numerous patches are calculated using manual segmentations through the K-means algorithm. We demonstrate the efficiency of learning the simple pattern from each cluster; meanwhile, the mapping function for each cluster is determined by the patches in the training images and their corresponding manual segmentation patches. Two publicly recognized benchmark data sets, namely DRIVE and STARE, are used in our experimental validation. Experimental results show that the proposed approach outperforms conventional methods for vessel segmentation problems validated via public benchmark data sets, i.e., DRIVE and STARE.