Monitoring glaucomatous progression: classification of visual field measurements using stable reference data

  • Authors:

  • Shuanghui Meng;Mihai Lazarescu;Jim Ivins;Andrew Turpin

  • Affiliations:

  • Department of Computing, Curtin University of Technology, Perth, Western Australia;Department of Computing, Curtin University of Technology, Perth, Western Australia;Department of Computing, Curtin University of Technology, Perth, Western Australia;School of Computing Science & Information Technology, RMIT, Melbourne, Victoria, Australia

  • Venue:
  • ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
  • Year:
  • 2005

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Abstract

Glaucoma is a common disease of the eye that often results in partial blindness. The main symptom of glaucoma is the progressive deterioration of the visual field. Glaucoma management involves monitoring the progress of the disease using regular visual field tests but currently there is no standard method for classifying changes in visual field measurements. Sequence matching techniques typically rely on similarity measures. However, visual field measurements are very noisy, particularly in people with glaucoma. It is therefore difficult to establish a reference data set including both stable and progressive visual fields. We describe method that uses a baseline computed from a query sequence, to match stable sequences in a database collected from volunteers. The results suggest that the new method is more accurate than other techniques for identifying progressive sequences, though there is a small penalty for stable sequences.