Paper Info
Title
A manually-labeled, artery/vein classified benchmark for the DRIVE dataset
Abstract
The classification of retinal vessels into arteries and veins is an important step for the analysis of retinal vascular trees, for which the scientists have proposed several classification methods. An obvious concern regarding the strength of these methodologies is the closeness of the result of a particular method to the gold standard. Unfortunately, the research community lacks benchmarks, resulting in increased subjective error, biased opinion and an uncertain progress. This paper introduces a manually-labeled, artery/vein categorized gold standard image database, as an extension of the most widely used image set DRIVE. The labeling criterion is set after a careful analysis of the physiological facts about the retinal vascular system. In addition, the labeling process also includes several versions of original images to get certainty. A two-step validation phase consists of verification from the trained computer vision observers and a professional ophthalmologist, followed by a comparison with a gold standard set for the junction locations introduced in V4-Like filters. Our gold standard is in highly reliable form; offers research community for the result comparison and progress evaluation.
Year
DOI
Venue
2013
10.1109/CBMS.2013.6627847
Computer-Based Medical Systems
Keywords
Field
DocType
benchmark testing,biomedical optical imaging,blood vessels,computer vision,eye,filtering theory,image classification,image colour analysis,medical image processing,physiology,visual databases,V4-Like filters,classification methods,gold standard set,image set DRIVE dataset,junction locations,labeling criterion,manually-labeled artery-vein categorized gold standard image database,manually-labeled artery-vein classified benchmark,original image versions,physiological facts,professional ophthalmologist,research community,retinal vascular system,retinal vascular trees analysis,retinal vessel classification,subjective error,trained computer vision observers,two-step validation phase,Artery/Vein Classification,DRIVE,Vessels classification
Computer vision,Data mining,Computer science,Artificial intelligence,Image database,Contextual image classification,Filtering theory,Gold standard,Benchmark (computing)
Conference
Citations 
PageRank 
References 
8
0.54
4
Authors
4
Name
Order
Citations
PageRank
Touseef Ahmad Qureshi1101.60
Maged Habib2211.89
Andrew Hunter3254.40
Bashir Al-diri4417.50