Paper Info
Title
Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood.
Abstract
Early detection of glaucoma is important to slow down or cease progression of the disease and for preventing total blindness. We have previously proposed an automated scheme for detection of retinal nerve fiber layer defect (NFLD), which is one of the early signs of glaucoma observed on retinal fundus images. In this study, a new multi-step detection scheme was included to improve detection of subtle and narrow NFLDs. In addition, new features were added to distinguish between NFLDs and blood vessels, which are frequent sites of false positives (FPs). The result was evaluated with a new test dataset consisted of 261 cases, including 130 cases with NFLDs. Using the proposed method, the initial detection rate was improved from 82% to 98%. At the sensitivity of 80%, the number of FPs per image was reduced from 4.25 to 1.36. The result indicates the potential usefulness of the proposed method for early detection of glaucoma.
Year
DOI
Venue
2016
10.1117/12.2216662
Proceedings of SPIE
Keywords
Field
DocType
Retinal fundus images,glaucoma,retinal nerve fiber layer defects,computerized detection,false positive reduction,retinal vessels
Total blindness,Early detection,Computer vision,Glaucoma,Nerve fiber layer,Fundus (eye),Optics,Artificial intelligence,Retinal,Physics,False positive paradox
Conference
Volume
ISSN
Citations 
9785
0277-786X
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Chisako Muramatsu131735.56
kyoko ishida201.01
Akira Sawada314620.99
Y Hatanaka427624.77
Tetsuya Yamamoto501.35
Hiroshi Fujita611824.65