June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Architecture for Angle Closure Glaucoma: Novel methods for Imaging, Risk Assessment and Screening (AGAR)
Author Affiliations & Notes
  • Jimmy Jiang Liu
    Insittute for Infocomm Research, Singapore, Singapore
  • Yanwu Xu
    Insittute for Infocomm Research, Singapore, Singapore
  • Beng Hai Lee
    Insittute for Infocomm Research, Singapore, Singapore
  • Min Thet Htoo
    Insittute for Infocomm Research, Singapore, Singapore
  • Damon Wong
    Insittute for Infocomm Research, Singapore, Singapore
  • Baskaran Mani
    Singapore Eye Research Insttute, Singapore, Singapore
  • Tin Aung
    Singapore Eye Research Insttute, Singapore, Singapore
  • Tien Wong
    Singapore Eye Research Insttute, Singapore, Singapore
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3575. doi:
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    • Get Citation

      Jimmy Jiang Liu, Yanwu Xu, Beng Hai Lee, Min Thet Htoo, Damon Wong, Baskaran Mani, Tin Aung, Tien Wong; Architecture for Angle Closure Glaucoma: Novel methods for Imaging, Risk Assessment and Screening (AGAR). Invest. Ophthalmol. Vis. Sci. 2013;54(15):3575.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

To introduce AGAR, a system to automatically identify glaucoma type with OCT (optical coherence tomography) images.

 
Methods
 

We developed an automatic system to identify glaucoma type with OCT (optical coherence tomography) images. Glaucoma is classified according to the configuration of the angle (the part of the eye between the cornea and iris mainly responsible for drainage of aqueous humor) into open angle (OA) and angle-closure (AC) glaucoma. The AGAR system is based on image processing and machine learning (Fig. 1), which differs from previous works that rely on anterior chamber angle (ACA) assessment metrics used clinically, such as AOD (angle-opening distance) [Leung, et al, Arch. Ophthalmol. 2006], TIA (trabecular-iris angle) [Leung, et al, Invest. Ophthalmol. Vis. Sci. 2008], TISA (trabecular-iris space area) [Tian, et al, TBME 2011] and SLBA (Schwalbe’s line bounded area) [Tian, et al, EMBC 2010]. The system was tested on 2048 high definition OCT images from the Singapore National Eye Centre, which were manually annotated by trained graders from the Singapore Eye Research Institute.

 
Results
 

The 2048 images are from 8 circular scan videos of 8 patient eyes with glaucoma, 4 of them with PACG and other 4 with POAG. Each video contains 128 frames, and each frame is split into 2 images since it contains two angles. For the evaluation, we follow the widely used leave-one-out (LOO) method, i.e., for each testing round, 512 images from one PACG and one POAG patients are used for testing while others are used for training, thus 16 rounds are performed to test all cases. The proposed automated system achieves an AUC value of 0.921±0.036 and a combined accuracy of 84.0±5.7.

 
Conclusions
 

Using an innovative image processing and machine learning approach, AGAR system is able to achieve a good performance in angle closure assessment. The testing results on an extensive dataset are promising and demonstrate that the AGAR system has the potential to be expanded into an automated screening tool for primary angle closure glaucoma (PACG) detection.

 
 
Fig. 1: Flowchart of the proposed AGAR system.
 
Fig. 1: Flowchart of the proposed AGAR system.
 
Keywords: 549 image processing • 550 imaging/image analysis: clinical • 420 anterior chamber  
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