Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

Sarni Suhaila Rahim, Vasile Palade, James Shuttleworth, Chrisina Jayne

Research output: Contribution to journalArticle

29 Citations (Scopus)
3 Downloads (Pure)

Abstract

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.
Original languageEnglish
Pages (from-to)249-267
Number of pages18
JournalBrain Informatics
Volume3
Issue number4
Early online date16 Mar 2016
DOIs
Publication statusPublished - Dec 2016

Keywords

  • Diabetic retinopathy
  • Maculopathy
  • Eye screening
  • Colour fundus images
  • Fuzzy image processing

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