NDLI: Automated anterior chamber angle localization and glaucoma type classification in OCT images

Content Provider	IEEE Xplore Digital Library
Author	Yanwu Xu Jiang Liu Jun Cheng Beng Hai Lee Wong, D.W.K. Baskaran, M. Perera, S. Aung, T.
Copyright Year	2013
Description	Author affiliation: Inst. for Infocomm Res., Agency for Sci., Technol. & Res., Singapore, Singapore (Yanwu Xu; Jiang Liu; Jun Cheng; Beng Hai Lee; Wong, D.W.K.) \|\| Singapore Eye Res. Inst., Singapore, Singapore (Baskaran, M.; Perera, S.; Aung, T.)
Abstract	To identify glaucoma type with OCT (optical coherence tomography) images, we present an image processing and machine learning based framework to localize and classify anterior chamber angle (ACA) accurately and efficiently. In digital OCT photographs, our method automatically localizes the ACA region, which is the primary structural image cue for clinically identifying glaucoma type. Next, visual features are extracted from this region to classify the angle as open angle (OA) or angle-closure (AC). This proposed method has three major contributions that differ from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, it can directly classify ACA as OA/AC based on only visual features, which is different from previous work for ACA measurement that relies on clinical features. Third, it demonstrates that higher dimensional visual features outperform low dimensional clinical features in terms of angle closure classification accuracy. From tests on a clinical dataset comprising of 2048 images, the proposed method only requires 0.26s per image. The framework achieves a 0.921 ± 0.036 AUC (area under curve) value and 84.0% ± 5.7% balanced accuracy at a 85% specificity, which outperforms existing methods based on clinical features.
Starting Page	7380
Ending Page	7383
File Size	5273244
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611263
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Visualization Accuracy Image edge detection Image segmentation Optical imaging Blindness
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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