Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework

Abstract Accurate vessel segmentation is a fundamental and challenging task for the retinal fundus image analysis. Current approaches typically train a global discriminative model for retinal vessel classification that is difficult to fit the complex pattern of vessel structure. In this paper, we propose a novel divide-and-conquer funnel-structured classification framework for retinal vessel segmentation. More specifically, a dividing algorithm, named multiplex vessel partition (MVP), is proposed to divide retinal vessel into well constrained subsets where vessel pixels with similar geometrical property are grouped. A set of homogeneous classifiers are trained in parallel to form discriminative decision for each group. This decomposes a complex classification problem into a number of relatively simpler ones. Moreover, a funnel-structured vessel segmentation (FsVS) framework is proposed to reclassify the uncertain samples caused by imperfect grouping of pixels. This alleviates the problem in data partition at the dividing phase and further enhances the complexity and discriminative capability of the decision model. Both quantitative and qualitative experimental comparisons on three publicly available databases show that the proposed framework produces high performance for retinal vessel segmentation, achieving 95.47–96.46% vessel segmentation accuracy, 83.72–85.79% local vessel segmentation accuracy, 78.63–81.92% F1-score and 76.55–80.13% Matthew correlation coefficient respectively, better than the state-of-the-art methods.