Automatic Detection Of Microaneurysms In Colour Fundus Images For Diabetic Retinopathy Screening

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

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.
Original languageEnglish
Article number1149
Number of pages15
JournalNeural Computing and Applications
Volume27
Issue number5
Early online date7 Jun 2015
DOIs
Publication statusPublished - Jul 2016

Keywords

  • Artificial Intelligence (incl. Robotics)
  • Data Mining and Knowledge Discovery
  • Probability and Statistics in Computer Science
  • Computational Science and Engineering
  • Image Processing and Computer Vision
  • Computational Biology/Bioinformatics

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