Efficient reconstruction-based optic cup localization for glaucoma screening

Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Jiang Liu, Dong Xu

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

42 Citations (Scopus)

Abstract

We present a reconstruction-based learning technique to localize the optic cup in fundus images for glaucoma screening. In contrast to previous approaches which rely on low-level visual cues, our method instead considers the input image as a whole and infers its optic cup parameters from a codebook of manually labeled reference images based on their similarity to the input and their contribution towards reconstructing the input image. We show that this approach can be formulated as a closed-form solution without any search, which leads to highly efficient and 100% repeatable computation. Our tests on the ORIGA and SCES datasets show that the performance of this method compares favorably to those of previous techniques while operating at faster speeds. This suggests much promise for this approach to be used in practice for screening.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages445-452
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 22 Sep 201326 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/TerritoryJapan
CityNagoya
Period22/09/1326/09/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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