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Classification and Localisation of Diabetic-Related Eye Disease

  • Alireza Osareh
  • Majid Mirmehdi
  • Barry Thomas
  • Richard Markham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)

Abstract

Retinal exudates are a characteristic feature of many retinal diseases such as Diabetic Retinopathy. We address the development of a method to quantitatively diagnose these random yellow patches in colour retinal images automatically. After a colour normalisation and contrast enhancement preprocessing step, the colour retinal image is segmented using Fuzzy C-Means clustering. We then classify the segmented regions into two disjoint classes, exudates and non-exudates, comparing the performance of various classifiers. We also locate the optic disk both to remove it as a candidate region and to measure its boundaries accurately since it is a significant landmark feature for ophthalmologists. Three different approaches are reported for optic disk localisation based on template matching, least squares are estimation and snakes. The system could achieve an overall diagnostic accuracy of 90.1% for identification of the exudate pathologies and 90.7% for optic disk localisation.

Keywords

Optic Disk Retinal Image Gradient Vector Flow Scale Conjugate Gradient Gradient Vector Flow Snake 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alireza Osareh
    • 1
  • Majid Mirmehdi
    • 1
  • Barry Thomas
    • 1
  • Richard Markham
    • 2
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK
  2. 2.Bristol Eye HospitalBristolUK

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