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An Automated System for Detecting and Measuring Nailfold Capillaries

  • Michael Berks
  • Phil Tresadern
  • Graham Dinsdale
  • Andrea Murray
  • Tonia Moore
  • Ariane Herrick
  • Chris Taylor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Nailfold capillaroscopy is an established qualitative technique in the assessment of patients displaying Raynaud’s phenomenon. We describe a fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach. On an unseen set of 455 images, the system detects and locates individual capillaries as well as human experts, and makes measurements of vessel morphology that reveal statistically significant differences between patients with (relatively benign) primary Raynaud’s phenomenon, and those with potentially life-threatening systemic sclerosis.

Keywords

Random Forest Apical Width Raynauds Phenomenon Nailfold Capillaroscopy Regression Forest 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Michael Berks
    • 1
  • Phil Tresadern
    • 1
  • Graham Dinsdale
    • 1
  • Andrea Murray
    • 1
  • Tonia Moore
    • 1
  • Ariane Herrick
    • 1
  • Chris Taylor
    • 1
  1. 1.University of ManchesterManchesterUnited Kingdom

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