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

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Abstract

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.

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Acknowledgment

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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Authors and Affiliations

  1. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

    Devanjali Relan, Lucia Ballerini & Tom MacGillivray

  2. Computing, School of Science and Engineering, University of Dundee, Dundee, UK

    Emanuele Trucco

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  1. Devanjali Relan
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  2. Lucia Ballerini
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Correspondence to Devanjali Relan.

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Relan, D., Ballerini, L., Trucco, E. et al. Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vessels. Multimed Tools Appl 78, 12783–12803 (2019). https://doi.org/10.1007/s11042-018-6474-7

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