Title
Superpixel classification based optic cup segmentation.
Abstract
In this paper, we propose a superpixel classification based optic cup segmentation for glaucoma detection. In the proposed method, each optic disc image is first over-segmented into superpixels. Then mean intensities, center surround statistics and the location features are extracted from each superpixel to classify it as cup or non-cup. The proposed method has been evaluated in one database of 650 images with manual optic cup boundaries marked by trained professionals and one database of 1676 images with diagnostic outcome. Experimental results show average overlapping error around 26.0% compared with manual cup region and area under curve of the receiver operating characteristic curve in glaucoma detection at 0.811 and 0.813 in the two databases, much better than other methods. The method could be used for glaucoma screening.
Year
DOI
Venue
2013
10.1007/978-3-642-40760-4_53
Lecture Notes in Computer Science
Keywords
Field
DocType
working paper
Computer vision,Glaucoma,Receiver operating characteristic,Pattern recognition,Segmentation,Glaucoma screening,Computer science,Optic disc,Artificial intelligence,Optic cup (anatomical)
Conference
Volume
Issue
ISSN
8151
Pt 3
0302-9743
Citations 
PageRank 
References 
8
0.61
9
Authors
7
Name
Order
Citations
PageRank
Jun Cheng121420.65
Jiang Liu229942.50
Dacheng Tao319032747.78
Fengshou Yin41259.66
Damon Wing Kee Wong543437.78
Yanwu Xu644740.32
Tien Yin Wong738938.10