Blood vessel segmentation of fundus images via cross-modality dictionary learning

Automated retinal blood vessel segmentation is important for the early computer-aided diagnosis of some ophthalmological diseases and cardiovascular disorders. Traditional supervised vessel segmentation methods are usually based on pixel classification, which categorizes all pixels into vessel and non-vessel pixels. In this paper, we propose a new retinal vessel segmentation method with the motivation to extract vessels based on vessel block segmentation via cross-modality dictionary learning. For this, we first enhance the structural information of vessels using multi-scale filtering. Then, cross-modality description and segmentation dictionaries are learned to build the intrinsic relationship between the enhanced vessels and the labeled ground truth vessels for the purpose of vessel segmentation. Also, effective pre-processing and post-processing are adopted to promote the performance. Experimental results on three benchmark data sets demonstrate that the proposed method can achieve good segmentation results.