Image segmentation of biofilm structures using optimal multi-level thresholding

Authors:
Dario Rojas;Luis Rueda;Alioune Ngom;Homero Hurrutia;Gerardo Carcamo
Affiliations:
Department of Computer Science, University of Atacama, 485 Copayapu Ave., Copiapo 1532296, Chile.;School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON N9B 3P4, Canada.;School of Computer Science, University of Windsor, 401 Sunset Ave., Windsor, ON N9B 3P4, Canada.;Center for Biotechnology and Faculty of Biological Sciences, University of Concepcion, 4070386, Chile.;Center for Biotechnology and Faculty of Biological Sciences, University of Concepcion, 4070386, Chile
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2011

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Abstract

The appreciation of biofilm structures in digital images can be subjective to the observer, and hence it is necessary to analyse the underlying images in useful parameters by means of quantification that is, ideally, free of errors. This paper proposes a combination of techniques for segmentation of biofilm images through an optimal multi-level thresholding algorithm and a set of clustering validity indices, including the determination of the best number of thresholds. The results, which are validated through Rand Index and a quantification process performed in a laboratory, are similar to the quantification and segmentation done by an expert.