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An Evaluation of Image Enhancement Techniques for Nailfold Capillary Skeletonisation

N. Doshi,G. Schaefer,S. Zhu

Published 2015 in International Conference on Knowledge-Based Intelligent Information & Engineering Systems

ABSTRACT

Abstract Nailfold capillaroscopy (NC) is a routine technique used to assess the characteristics and morphology of nailfold capillaries. Observation of micro-blood vessels in the nailfold is important for diagnosing diseases that lead to morphological changes of capillaries such as scleroderma, Raynaud's phenomenon and other connective tissue diseases. In order to support a computer-aided diagnosis approach to analysing NC images, several approaches have been proposed in the literature aiming to extract capillaries. In general, such capillary skeletonisation algorithms involve an image pre-processing step, followed by binarisation and finally extraction and definition of the capillary skeletons. Since image denoising and enhancement in the pre-processing step can have a major impact on the subsequent analysis, in this paper, we evaluate the performance of five enhancement techniques for the purpose for nailfold capillary skeletonisation. In particular, we investigate the α-trimmed filter, bilateral filter, bilateral enhancer, anisotropic diffusion filter and non-local means and integrate them with three capillary extraction algorithms from the literature. We report visual and quantitative performance on a set of diverse NC images. The obtained results indicate that a relatively simple α-trimmed filter, combined with a skeletonisation algorithm incorporating a difference-of-Gaussian approach to address non-uniform lighting and an iterative rule-based skeletonisation procedure, leads to the best results when comparing the obtained skeletonisations to a manually obtained ground truth.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    International Conference on Knowledge-Based Intelligent Information & Engineering Systems

  • Publication date

    Unknown publication date

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    DOI 10.1016/j.procs.2015.08.271
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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