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Environmental microbiology aided by content-based image analysis

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Abstract

Environmental microorganisms (EMs) such as bacteria and protozoa are found in every imaginable environments. To explore functions of EMs is an important research field for environmental assessment and treatment. However, EMs are traditionally investigated through morphological analysis using microscopes or DNA analysis, which is time and money consuming. To overcome this, we introduce an innovative method which applies content-based image analysis (CBIA) to environmental microbiology. Our method classifies EMs into different categories based on features extracted from microscopic images. Specifically, it consists of three steps: The first is image segmentation which accurately extracts the region of an EM in a microscopic image with a small amount of user interaction. The second step is feature extraction where multiple features are extracted to describe different characteristics of the EM. In particular, we develop an internal structure histogram descriptor which captures the structure of the EM using angles defined on its contour. The last step is fusion which combines classification results by different features to improve the performance. Experimental results validate the effectiveness and practicability of our environmental microbiology method aided by CBIA.

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Acknowledgments

Research activities leading to this work have been supported by the China Scholarship Council and the Japan Society for the Promotion of Science. We greatly thank Prof. Dr. Beihai Zhou and Dr. Fangshu Ma from the University of Science and Technology Beijing for providing us with image dataset for experiments. Moreover, we are also very grateful to Dr. Joanna Czajkowska, Dipl.-Inform. Christian Feinen, M. A. Cathrin Warnke, Mr. Florian Schmidt and Mr. Oliver Tiebe from the University of Siegen for their guiding on significant technologies.

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  1. Research Group for Pattern Recognition, University of Siegen, Hoelderlinstr. 3, 57076, Siegen, Germany

    Chen Li, Kimiaki Shirahama & Marcin Grzegorzek

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  1. Chen Li
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  2. Kimiaki Shirahama
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  3. Marcin Grzegorzek
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Correspondence to Chen Li.

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Li, C., Shirahama, K. & Grzegorzek, M. Environmental microbiology aided by content-based image analysis. Pattern Anal Applic 19, 531–547 (2016). https://doi.org/10.1007/s10044-015-0498-7

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