Multimedia Tools and Applications

, Volume 78, Issue 10, pp 12783–12803 | Cite as

Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vessels

  • Devanjali RelanEmail author
  • Lucia Ballerini
  • Emanuele Trucco
  • Tom MacGillivray


Automatically classifying retinal blood vessels appearing in fundus camera imaging into arterioles and venules can be problematic due to variations between people as well as in image quality, contrast and brightness. Using the most dominant features for retinal vessel types in each image rather than predefining the set of characteristic features prior to classification may achieve better performance. In this paper, we present a novel approach to classifying retinal vessels extracted from fundus camera images which combines an Orthogonal Locality Preserving Projections for feature extraction and a Gaussian Mixture Model with Expectation-Maximization unsupervised classifier. The classification rate with 47 features (the largest dimension tested) using OLPP on our own ORCADES dataset and the publicly available DRIVE dataset was \(90.56\%\) and \(86.7\%\) respectively.


Retina Fundus images Vessel classification Feature extraction Orthogonal locality preserving projections (OLPP) 



This work was supported by Leverhulme Trust grant RPG-419 “Discovery of retinal biomarkers for genetics with large cross-linked datasets”. Support from NHS Lothian R&D and the Edinburgh Clinical Research Imaging Centre is gratefully acknowledged.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
  2. 2.Computing, School of Science and EngineeringUniversity of DundeeDundeeUK

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