Retinal vasculature classification using novel multifractal features

Research output: Contribution to journalArticle


  • Y. Ding
  • W. O.C. Ward
  • D. P. Auer
  • Penny Gowland
  • L. Bai

Colleges, School and Institutes

External organisations

  • University of Nottingham


Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.


Original languageEnglish
Pages (from-to)8365-8379
Number of pages15
JournalPhysics in Medicine and Biology
Issue number21
Publication statusPublished - 12 Oct 2015


  • classification, feature extraction, multifractal analysis, retinal blood vessel