title: A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images.
abstract: Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to false positive lesion detection counts in a subsequent lesion analysis stage. In this study, a new hybrid method for the segmentation of the smallest vessels is proposed. Line detection and perceptual organization techniques are combined in a multi-scale scheme. Small vessels are reconstructed from the perceptual-based approach via tracking and pixel painting. The segmentation was validated in a high resolution fundus image database including healthy and diabetic subjects using pixel-based as well as perceptual-based measures. The proposed method achieves 85.06% sensitivity rate, while the original multi-scale line detection method achieves 81.06% sensitivity rate for the corresponding images (p<0.05). The improvement in the sensitivity rate for the database is 6.47% when only the smallest vessels are considered (p<0.05). For the perceptual-based measure, the proposed method improves the detection of the vasculature by 7.8% against the original multi-scale line detection method (p<0.05).
authors.affiliation: Polytechnique Montréal, Montréal, QC H3C 3A7, Canada. Electronic address: argyrios.christodoulidis@polymtl.ca.
authors.forename: Argyrios
authors.initials: A
authors.lastname: Christodoulidis
authors.affiliation: Polytechnique Montréal, Montréal, QC H3C 3A7, Canada. Electronic address: thomas.hurtut@polymtl.ca.
authors.forename: Thomas
authors.initials: T
authors.lastname: Hurtut
authors.affiliation: Diagnos Inc., Brossard, QC J4Z 1A7, Canada. Electronic address: hbentahar@diagnos.ca.
authors.forename: Houssem Ben
authors.initials: HB
authors.lastname: Tahar
authors.affiliation: Polytechnique Montréal, Montréal, QC H3C 3A7, Canada. Electronic address: farida.cheriet@polymtl.ca.
authors.forename: Farida
authors.initials: F
authors.lastname: Cheriet
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000000981:diagnostic imaging; D003952:Diagnostic Imaging / Q000379:methods*; D005654:Fundus Oculi; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012171:Retinal Vessels / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity
keywords: Diabetic retinopathy; Fundus imaging; Multi-scale line detection; Perceptual organization; Retinal blood vessel segmentation