Refractive error detection via group sparse representation

Qin Li, Jinghua Wang, Jia You, Bob Zhang, Fakhri Karray

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

nowadays large populations worldwide are suffering from eye diseases such as astigmatism, myopia, and hyperopia which are caused by ophthalmologically refractive errors. This paper presents an effective approach to computer aided diagnosis of such eye diseases due to ophthalmologically refractive errors. The proposed system consists of two major steps: (1) image segmentation and geometrical feature extraction; (2) group sparse representation based classification. Although image segmentation seems relatively easy and straight forward, it is a challenge task to achieve high accuracy of segmentation for images at poor quality caused by distortion during image digitization. To avoid misclassifications by incomplete information, we propose group sparse representation-based classification scheme to classify low-dimensional data which are partially corrupted. The experimental results demonstrate the feasibility of the new classification scheme with good performance for potential medical applications.
Original languageEnglish
Title of host publicationIEEE 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010
DOIs
Publication statusPublished - 28 Sep 2010
EventIEEE 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010 - Povoa de Varzim, Portugal
Duration: 21 Jun 201023 Jun 2010

Conference

ConferenceIEEE 2010 International Conference on Autonomous and Intelligent Systems, AIS 2010
CountryPortugal
CityPovoa de Varzim
Period21/06/1023/06/10

Keywords

  • Eye disease
  • Feature extraction
  • Group sparse classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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