Anterior Chamber Angle Classification Using Multiscale Histograms of Oriented Gradients for Glaucoma Subtype Identification

Anterior Chamber Angle Classification Using Multiscale Histograms of Oriented Gradients for Glaucoma Subtype Identification
Title:
Anterior Chamber Angle Classification Using Multiscale Histograms of Oriented Gradients for Glaucoma Subtype Identification
Other Titles:
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
DOI:
10.1109/EMBC.2012.6346637
Keywords:
Publication Date:
28 August 2012
Citation:
Yanwu Xu; Jiang Liu; Ngan Meng Tan; Beng Hai Lee; Wong, D.W.K.; Baskaran, M.; Perera, S.A.; Tin Aung, "Anterior chamber angle classification using multiscale histograms of oriented gradients for glaucoma subtype identification," in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE , vol., no., pp.3167-3170, Aug. 28 2012-Sept. 1 2012
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.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
1557-170X
978-1-4244-4119-8
ISBN:
978-1-4577-1787-1
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