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Semantic Reconstruction-Based Nuclear Cataract Grading from Slit-Lamp Lens Images

  • Yanwu XuEmail author
  • Lixin Duan
  • Damon Wing Kee Wong
  • Tien Yin Wong
  • Jiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Cataracts are the leading cause of visual impairment and blindness worldwide. Cataract grading, i.e. assessing the presence and severity of cataracts, is essential for diagnosis and progression monitoring. We present in this work an automatic method for predicting cataract grades from slit-lamp lens images. Different from existing techniques which normally formulate cataract grading as a regression problem, we solve it through reconstruction-based classification, which has been shown to yield higher performance when the available training data is densely distributed within the feature space. To heighten the effectiveness of this reconstruction-based approach, we introduce a new semantic feature representation that facilitates alignment of test and reference images, and include locality constraints on the linear reconstruction to reduce the influence of less relevant reference samples. In experiments on the large ACHIKO-NC database comprised of 5378 images, our system outperforms the state-of-the-art regression methods over a range of evaluation metrics.

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© Springer International Publishing AG 2016

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Authors and Affiliations

  • Yanwu Xu
    • 1
    Email author
  • Lixin Duan
    • 2
  • Damon Wing Kee Wong
    • 1
  • Tien Yin Wong
    • 3
  • Jiang Liu
    • 1
    • 4
  1. 1.Institute for Infocomm Research, Agency for Science, Technology and ResearchSingaporeSingapore
  2. 2.AmazonSeattleUSA
  3. 3.Singapore Eye Research InstituteSingaporeSingapore
  4. 4.Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesBeijingChina

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