Efficient Optimal Multi-level Thresholding for Biofilm Image Segmentation

  • Darío Rojas
  • Luis Rueda
  • Homero Urrutia
  • Alioune Ngom
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

A microbial biofilm is structured mainly by a protective sticky matrix of extracellular polymeric substances. The appreciation of such structures is useful for the microbiologist and can be subjective to the observer. Thus, quantifying the underlying images in useful parameters by means of an objective image segmentation process helps substantially to reduce errors in quantification. This paper proposes an approach to segmentation of biofilm images using optimal multilevel thresholding and indices of clustering validity. A comparison of automatically segmented images with manual segmentation is done through different thresholding criteria, and clustering validity indices are used to find the correct number of thresholds, obtaining results similar to the segmentation done by an expert.

Keywords

Image Segmentation Extracellular Polymeric Substance Automatic Segmentation Manual Segmentation Cluster Validity 
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

  • Darío Rojas
    • 1
  • Luis Rueda
    • 2
  • Homero Urrutia
    • 3
  • Alioune Ngom
    • 2
  1. 1.Department of Computer ScienceUniversity of AtacamaCopiapóChile
  2. 2.School of Computer ScienceUniversity of WindsorWindsorCanada
  3. 3.Biotechnology Center and Faculty of Biological SciencesUniversity of ConcepciónConcepciónChile

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