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Open Access Methodology article

Bacterial cell identification in differential interference contrast microscopy images

Boguslaw Obara1*, Mark AJ Roberts23, Judith P Armitage23 and Vicente Grau45

Author Affiliations

1 School of Engineering and Computing Sciences, University of Durham, Durham, UK

2 Oxford Centre for Integrative Systems Biology, University of Oxford, Oxford, UK

3 Department of Biochemistry, University of Oxford, Oxford, UK

4 Oxford e-Research Centre, University of Oxford, Oxford, UK

5 Institute of Biomedical Engineering, University of Oxford, Oxford, UK

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BMC Bioinformatics 2013, 14:134  doi:10.1186/1471-2105-14-134

Published: 23 April 2013

Abstract

Background

Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.

Results

We have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides.

Conclusions

Results demonstrate that the proposed approach provides a fast and robust method for detection and analysis of spatial relationship between bacterial cells and their chemotaxis proteins.