Medical & Biological Engineering & Computing

, Volume 50, Issue 6, pp 567–574

Quantitative color analysis for capillaroscopy image segmentation

  • Michela Goffredo
  • Maurizio Schmid
  • Silvia Conforto
  • Beatrice Amorosi
  • Tommaso D’Alessio
  • Claudio Palma
Original Article

DOI: 10.1007/s11517-012-0907-7

Cite this article as:
Goffredo, M., Schmid, M., Conforto, S. et al. Med Biol Eng Comput (2012) 50: 567. doi:10.1007/s11517-012-0907-7

Abstract

This communication introduces a novel approach for quantitatively evaluating the role of color space decomposition in digital nailfold capillaroscopy analysis. It is clinically recognized that any alterations of the capillary pattern, at the periungual skin region, are directly related to dermatologic and rheumatic diseases. The proposed algorithm for the segmentation of digital capillaroscopy images is optimized with respect to the choice of the color space and the contrast variation. Since the color space is a critical factor for segmenting low-contrast images, an exhaustive comparison between different color channels is conducted and a novel color channel combination is presented. Results from images of 15 healthy subjects are compared with annotated data, i.e. selected images approved by clinicians. By comparison, a set of figures of merit, which highlights the algorithm capability to correctly segment capillaries, their shape and their number, is extracted. Experimental tests depict that the optimized procedure for capillaries segmentation, based on a novel color channel combination, presents values of average accuracy higher than 0.8, and extracts capillaries whose shape and granularity are acceptable. The obtained results are particularly encouraging for future developments on the classification of capillary patterns with respect to dermatologic and rheumatic diseases.

Keywords

Capillaroscopy Segmentation Color analysis Color space 

Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  • Michela Goffredo
    • 1
  • Maurizio Schmid
    • 1
  • Silvia Conforto
    • 1
  • Beatrice Amorosi
    • 2
  • Tommaso D’Alessio
    • 1
  • Claudio Palma
    • 3
  1. 1.Department of Applied ElectronicsUniversity “Roma TRE”RomeItaly
  2. 2.IFO San Gallicano Dermatology Institute, IRCCSRomeItaly
  3. 3.Department of PhysicsUniversity “Roma TRE”RomeItaly

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