Self-assessment for optic disc segmentation

Jun Cheng, Jiang Liu, Fengshou Yin, Beng Hai Lee, Damon Wing Kee Wong, Tin Aung, Ching Yu Cheng, Tien Yin Wong

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

5 Citations (Scopus)

Abstract

Optic disc segmentation from retinal fundus image is a fundamental but important step in many applications such as automated glaucoma diagnosis. Very often, one method might work well on many images but fail on some other images and it is difficult to have a single method or model to cover all scenarios. Therefore, it is important to combine results from several methods to minimize the risk of failure. For this purpose, this paper computes confidence scores for three methods and combine their results for an optimal one. The experimental results show that the combined result from three methods is better than the results by any individual method. It reduces the mean overlapping error by 7.4% relatively compared with best individual method. Simultaneously, the number of failed cases with large overlapping errors is also greatly reduced. This is important to enhance the clinical deployment of the automated disc segmentation.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages5861-5864
Number of pages4
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 3 Jul 20137 Jul 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Country/TerritoryJapan
CityOsaka
Period3/07/137/07/13

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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