Microaneurysm (MA) detection via sparse representation classifier with MA and non-MA dictionary learning

Bob Zhang, Kakhri Karray, Lei Zhang, Jia You

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

15 Citations (Scopus)

Abstract

Diabetic retinopathy (DR) is a common complication of diabetes that damages the retina and leads to sight loss if treated late. In its earliest stage, DR can be diagnosed by microaneurysm (MA). Although some algorithms have been developed, the accurate detection of MA in color retinal images is still a challenging problem. In this paper we propose a new method to detect MA based on Sparse Representation Classifier (SRC). We first roughly locate MA candidates by using multi-scale Gaussian correlation filtering, and then classify these candidates with SRC. Particularly, two dictionaries, one for MA and one for non-MA, are learned from example MA and non-MA structures, and are used in the SRC process. Experimental results on the ROC database show that the proposed method can well distinguish MA from non-MA objects.
Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages277-280
Number of pages4
DOIs
Publication statusPublished - 18 Nov 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 23 Aug 201026 Aug 2010

Conference

Conference2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period23/08/1026/08/10

Keywords

  • Diabetic retinopathy
  • Microaneurysm
  • Sparse representation classifier

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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