Automatic vessel segmentation on fundus images using vessel filtering and fuzzy entropy

Vessel segmentation is a critical and challenging task for fundus image processing, which is precursor and essential first step to further vessel measurement and diagnosis. This paper proposes a novel hybrid automatic vessel segmentation method for the delineation of vessels on fundus images. The method consists of two main steps including Hessian-based vessel filtering and vessel segmentation. In vessel filtering, multi-scale linear filtering based on Hessian matrix is adapted to enhance vessels in the image. After vessel filtering, a novel two-dimensional histogram of filtering image is generated. Then, the thresholds are determined by the fuzzy entropic concepts. We demonstrate the effectiveness of the proposed method on real fundus images from DRIVE database. Quantification analysis is applied through three metrics with respect to manual delineated ground truth from one specialist. Compared to three other methods, the proposed method yields more complete and accurate results.