April 2010
Volume 51, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2010
Assessment of a Computerized Nuclear Cataract Grading System
H. Li; J. Lim; J. Liu; D. Wong; N. Tan; C. Y. Cheung; P. Mitchell; A. G. Tan; J. J. Wang; T. Y. Wong
Author Affiliations & Notes
  • H. Li
    Institute for Infocomm Research, Singapore, Singapore
  • J. Lim
    Institute for Infocomm Research, Singapore, Singapore
  • J. Liu
    Institute for Infocomm Research, Singapore, Singapore
  • D. Wong
    Institute for Infocomm Research, Singapore, Singapore
  • N. Tan
    Institute for Infocomm Research, Singapore, Singapore
  • C. Y. Cheung
    Level 5, SNEC Building, Singapore Eye Research Institute, Singapore, Singapore
  • P. Mitchell
    Ophthalmology, University of Sydney, Sydney, Australia
  • A. G. Tan
    Centre for Vision Research,
    University of Sydney, Westmead, Australia
  • J. J. Wang
    Ctr for Vision Research/Ophthalmol,
    University of Sydney, Westmead, Australia
  • T. Y. Wong
    Singapore Eye Research Institute, National University of Singapore, Singapore, Singapore
  • Footnotes
    Commercial Relationships  H. Li, None; J. Lim, Method and System for Automatic Grading of Nuclear Cataract, patent filed on Aug,24 2009, P; J. Liu, Method and System for Automatic Grading of Nuclear Cataract, patent filed on Aug,24 2009, P; D. Wong, Method and System for Automatic Grading of Nuclear Cataract, patent filed on Aug,24 2009, P; N. Tan, Method and System for Automatic Grading of Nuclear Cataract, patent filed on Aug,24 2009, P; C.Y. Cheung, None; P. Mitchell, None; A.G. Tan, None; J.J. Wang, None; T.Y. Wong, Method and System for Automatic Grading of Nuclear Cataract, patent filed on Aug,24 2009, P.
  • Footnotes
    Support  Singapore Bio Imaging Consortium (SBIC) Grant C-011/2006
Investigative Ophthalmology & Visual Science April 2010, Vol.51, 5666. doi:
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      H. Li, J. Lim, J. Liu, D. Wong, N. Tan, C. Y. Cheung, P. Mitchell, A. G. Tan, J. J. Wang, T. Y. Wong; Assessment of a Computerized Nuclear Cataract Grading System. Invest. Ophthalmol. Vis. Sci. 2010;51(13):5666.

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Abstract

Purpose: : To develop and test an automatic grading system for nuclear cataract using slit-lamp images.

Methods: : Our system was developed using digital slit-lamp images acquired with a digital Topcon DC-1 slit-lamp camera. The automatic nuclear cataract system consists of a segmentation algorithm that detect the anatomical structure of lens, a feature extraction algorithm, and a grade prediction algorithm. Anatomical structure consisting of the contour of lens and contour of nuclear region is detected using a modified active shape model. The 21-dimensional features to assess nuclear opacity were selected according to clinical grading protocol, which include intensity of sulcus, intensity ratio between nucleus and lens, and strength of nucleus edge. Support vector machine regression is employed for grade prediction, in which one hundred images were used as the training set.

Results: : The automatic grading system was tested using 5551 slit-lamp images from a population-based study, the Singapore Malay Eye Study. The lens structure was detected successfully for 95% of the tested images. Compared with the Wisconsin cataract grading system, the average differences of the automatic grading system is 0.36 on a 5.0 scale. The t-value is 2.74 and the p-value is 0.006.

Conclusions: : An automatic grading system for nuclear cataract has been developed and tested. The system can achieve good agreement with the Wisconsin cataract grading system. The system will save workload for population study and improve objectivity of clinical grading.

Keywords: imaging/image analysis: clinical • cataract • image processing 
© 2010, The Association for Research in Vision and Ophthalmology, Inc., all rights reserved. Permission to republish any abstract or part of an abstract in any form must be obtained in writing from the ARVO Office prior to publication.
Copyright © 2024 Association for Research in Vision and Ophthalmology.
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