Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images

Authors:
M. Tscherepanow;F. Zöllner;M. Hillebrand;F. Kummert
Affiliations:
Applied Computer Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany D-33501;Computer Assisted Clinical Medicine, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany D-68167;Applied Computer Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany D-33501;Applied Computer Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany D-33501
Venue:
MDA '08 Proceedings of the 3rd international conference on Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry
Year:
2008

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Abstract

The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells.