Segmentation of microorganism in complex environments

Authors:
M. Kemmler;B. Fröhlich;E. Rodner;J. Denzler
Affiliations:
Chair for Computer Vision, Frendlich Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Frendlich Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Frendlich Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Frendlich Schiller University of Jena, Jena, Germany
Venue:
Pattern Recognition and Image Analysis
Year:
2013

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Abstract

In this paper, we tackle the problem of finding microorganisms in bright field microscopy images, which is an important and challenging step in various tasks, like classifying soil textures. Apart from bacteria or fungi, these images can contain impurities such as sand particles, which increase the difficulty of microbe detection. Following a semantic segmentation approach, where a label is inferred for each pixel, we achieve encouraging classification results on a database containing five different types of microbes. We review and evaluate multiple techniques including segment classification, conditional random field models, and level set approaches.