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Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering

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Abstract

Comet assay is one of the most popular tests for the detection of DNA damage at single cell level. In this study, an algorithm for comet assay analysis has been proposed, aiming to minimize user interaction and providing reproducible measurements. The algorithm comprises two-steps: (a) comet identification via Gaussian pre-filtering and morphological operators; (b) comet segmentation via fuzzy clustering. The algorithm has been evaluated using comet images from human leukocytes treated with a commonly used DNA damaging agent. A comparison of the proposed approach with a commercial system has been performed. Results show that fuzzy segmentation can increase overall sensitivity, giving benefits in bio-monitoring studies where weak genotoxic effects are expected.

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Authors and Affiliations

  1. University of Naples “Federico II”, via Claudio, 21, 80100, Naples, Italy

    Mario Sansone & Giovanni Esposito

  2. Institute for Electromagnetic Sensing of Environment (IREA), National Research Council, Naples, Italy

    Olga Zeni

Authors
  1. Mario Sansone
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  2. Olga Zeni
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  3. Giovanni Esposito
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Correspondence to Mario Sansone.

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Sansone, M., Zeni, O. & Esposito, G. Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering. Med Biol Eng Comput 50, 523–532 (2012). https://doi.org/10.1007/s11517-012-0882-z

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  • DOI: https://doi.org/10.1007/s11517-012-0882-z

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