NDLI: Anterior chamber angle classification using multiscale histograms of oriented gradients for glaucoma subtype identification

Content Provider	IEEE Xplore Digital Library
Author	Yanwu Xu Jiang Liu Ngan Meng Tan Beng Hai Lee Wong, D.W.K. Baskaran, M. Perera, S.A. Tin Aung
Copyright Year	2012
Description	Author affiliation: Inst. for Infocomm Res., Agency for Sci., Technol. & Res., Singapore, Singapore (Yanwu Xu; Jiang Liu; Ngan Meng Tan; Beng Hai Lee; Wong, D.W.K.) \|\| Singapore Eye Res. Inst., Singapore, Singapore (Baskaran, M.; Perera, S.A.; Tin Aung)
Abstract	Glaucoma subtype can be identified according to the configuration of the anterior chamber angle(ACA). In this paper, we present an ACA classification approach based on histograms of oriented gradients at multiple scales. In digital optical coherence tomography (OCT) photographs, our method automatically localizes the ACA, and extracts histograms of oriented gradients (HOG) features from this region to classify the angle as an open angle (OA) or an angle-closure(AC). This proposed method has three major features that differs from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, the ACA is directly classified as OA/AC by using multiscale HOG visual features only, which is different from previous ACA assessment approaches that on clinical features. Third, it demonstrates that visual features with higher dimensions outperform low dimensional clinical features in terms of angle closure classification accuracy. Testing was performed on a large clinical dataset, comprising of 2048 images. The proposed method achieves a 0.835±0.068 AUC value and 75.8% ± 6.4% balanced accuracy at a 85% specificity, which outperforms existing ACA classification approaches based on clinical features.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	3167
Ending Page	3170
File Size	1695555
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346637
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Visualization Accuracy Cornea Image edge detection Histograms Iris
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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