Fusing geometric and appearance-based features for glaucoma diagnosis

Kangrok Oh, Jooyoung Kim, Sangchul Yoon, Kyoung Yul Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose to fuse geometric and appearance-based features at the feature-level for automatic glaucoma diagnosis. The cup-to-disc ratio and neuro-retinal rim width variation are extracted as the geometric features based on a coarseto- fine localization method. For the appearancebased feature extraction, the principal components analysis is adopted. Finally, these features are combined at the feature-level based on the random projection and the total error rate minimization classifier. Experimental results on an in-house data set shows that the feature-level fusion can enhance the classification performance comparing with that before fusion.

Original languageEnglish
Title of host publication4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017
PublisherEuropean Association of Geoscientists and Engineers, EAGE
Pages76-85
Number of pages10
ISBN (Electronic)9781941968437
Publication statusPublished - 2017
Event4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017 - Lodz, Poland
Duration: 2017 Sep 182017 Sep 20

Publication series

Name4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017

Other

Other4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2017
Country/TerritoryPoland
CityLodz
Period17/9/1817/9/20

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2016-31-0650).

Publisher Copyright:
© 2017 SDIWC.

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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