NDLI: Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Fengshou Yin Jiang Liu Wong, D.W.K. Ngan Meng Tan Cheung, C. Baskaran, M. Tin Aung Tien Yin Wong
Copyright Year	2012
Description	Author affiliation: Institute for Infocomm Research, A*STAR, Singapore (Fengshou Yin; Jiang Liu; Wong, D.W.K.; Ngan Meng Tan) \|\| Singapore Eye Research Institute, Singapore (Cheung, C.; Baskaran, M.; Tin Aung; Tien Yin Wong)
Abstract	The vertical Cup-to-Disc Ratio (CDR) is an important indicator in the diagnosis of glaucoma. Automatic segmentation of the optic disc (OD) and optic cup is crucial towards a good computer-aided diagnosis (CAD) system. This paper presents a statistical model-based method for the segmentation of the optic disc and optic cup from digital color fundus images. The method combines knowledge-based Circular Hough Transform and a novel optimal channel selection for segmentation of the OD. Moreover, we extended the method to optic cup segmentation, which is a more challenging task. The system was tested on a dataset of 325 images. The average Dice coefficient for the disc and cup segmentation is 0.92 and 0.81 respectively, which improves significantly over existing methods. The proposed method has a mean absolute CDR error of 0.10, which outperforms existing methods. The results are promising and thus demonstrate a good potential for this method to be used in a mass screening CAD system.
Starting Page	1
Ending Page	6
File Size	915616
Page Count	6
File Format	PDF
ISBN	9781467320498
ISSN	10637125
e-ISBN	9781467320511
DOI	10.1109/CBMS.2012.6266344
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optical imaging Image segmentation Adaptive optics Shape Blood vessels Biomedical imaging Image edge detection
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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