Capillary Abnormalities Detection Using Vessel Thickness and Curvature Analysis

  • Mariusz Paradowski
  • Urszula Markowska-Kaczmar
  • Halina Kwasnicka
  • Krzysztof Borysewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5712)


The growing importance of nail-fold capillaroscopy imaging as a diagnostic tool in medicine increases the need to automate this process. One of the most important markers in capillaroscopy is capillary thickness. On this basis capillaries may be divided into three separate categories: healthy, capillaries with increased loops and megacapillaries. In the paper we describe the problem of capillary thickness analysis automation. First, data is extracted from a segmented capillary image. Then feature vectors are constructed. They are given as an input for capillary classification method. We applied different classifiers in the experiments. The best achieved accuracy reaches 97%, which can be considered as very high and satisfying.


Feature Vector Systemic Sclerosis Mixed Connective Tissue Disease Skeleton Point Nailfold Capillaroscopy 
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 2009

Authors and Affiliations

  • Mariusz Paradowski
    • 1
  • Urszula Markowska-Kaczmar
    • 1
  • Halina Kwasnicka
    • 1
  • Krzysztof Borysewicz
    • 2
  1. 1.Institute of InformaticsWroclaw University of TechnologyPoland
  2. 2.Department of Rheumatology and Internal DiseasesWroclaw Medical UniversityPoland

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