Pathological myopia detection from selective fundus image features

Zhuo Zhang, Jun Cheng, Jiang Liu, Yeo Cher May Sheri, Chui Chee Kong, Saw Seang Mei

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

7 Citations (Scopus)

Abstract

We explore feature selection methodology for automatic Pathological Myopia detection via learning from an optimal set of features. An mRMR optimized classifier is trained using the candidate feature set to find the optimized classifier. We tested the proposed methodology on eye records of approximately 800 subjects collected from a population study. The experimental results demonstrate that the new classifier is much efficient by using less than 25% of the initial candidate feature set. The ranked optimal feature set also enables the clinicians to better access the diagnostic process of the algorithm. The work is a further step towards the advancement of the automatic pathological myopia diagnosis.

Original languageEnglish
Title of host publicationProceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
Pages1742-1745
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 - Singapore, Singapore
Duration: 18 Jul 201220 Jul 2012

Publication series

NameProceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012

Conference

Conference2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
Country/TerritorySingapore
CitySingapore
Period18/07/1220/07/12

Keywords

  • Minimum Redundancy-Maximum Relevancy (mRMR)
  • Pathological Myopia
  • Support Vector Machines (SVM)
  • peripapillary atrophy (PPA)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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