Frangi based multi-scale level sets for retinal vascular segmentation. (December 2020)

• Record Type: Journal Article
• Authors: Yang, Jinzhu; Huang, Mingxu; Fu, Jie; Lou, Chunhui; Feng, Chaolu
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Retinal vascular segmentation -- Level set -- Hessian -- Multi-scale; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105752
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Two Frangi based level set models are proposed to segment retinal vessels. The Frangi filtering is used to enhance vascular structures in the image. The local scales make Frangi function taking its maximum response are considered as the optimal scales. The proposed models produce segmentation accuracy at the same level with state-of-the-art methods on DRIVE and STARE image repositories. Abstract: Retinal vascular disease has always been the focus of medical attention. However, segmentation of the retinal vessels from fundus images is still an open problem due to intensity inhomogeneity in the image and thickness diversity of the retinal vessels. In this paper, we propose Frangi based multi-scale level sets to segment retinal vessels from fundus images. Vascular structures are first enhanced by the Frangi filter with local optimal scales being obtained at the same time. The enhanced image and local optimal scales are taken considered as inputs of the proposed level set models. Effectiveness of the proposed multi-scale level sets to their scale fixed versions has been evaluated using DRIVE and STARE image repositories. In addition, the proposed level set models have been tested on the DRIVE and STARE images. Experiments show that the proposed models produce segmentation accuracy at the same level with state-of-the-art methods.