Semantic reconstruction-based nuclear cataract grading from slit-lamp lens images

Yanwu Xu, Lixin Duan, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
PublisherSpringer Verlag
Pages458-466
Number of pages9
ISBN (Print)9783319467252
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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