Simulated Biological Cells for Receptor Counting in Fluorescence Imaging
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Digital image processing and epifluorescence microscopy provide one of the main and basic tools for living biological cell analysis and studying. Developing, testing, and comparing those image processing methods properly is eased by the use of a controlled environment. Taking advantage of an existing database of verified and trustworthy images and meta-data helps controlling the validity of the processing results. Manually generating that golden database is a long process involving specialists being able to apprehend and extract useful data out of fluorescent images. Having enough cases in the database to challenge the processing methods and gain trust in them can only be achieved manually through time-consuming, prone to human-error processes. More and more we need to automate this process. This paper presents a framework implementing a novel approach to generate synthetic fluorescent images of fluorescently stained cell populations by simulating the imaging process of fluorescent molecules. Ultimately, the proposed simulator allows us to generate images and golden data to populate the database, thus providing tools for the development, evaluation, and testing of processing algorithms meant to be used in automated systems.
KeywordsBiological image processing Fluorescence imaging Cell simulation Synthetic image Simulator Deconvolution
The authors would like to acknowledge Paolo Scilacci and Linda Corbino-Giunta for the Caco-2 cell samples preparation. (Fig. 1). This work is supported by the Nutri-CHIP project, which is financed with a grant form the Swiss Nano-Tera.ch initiative and evaluated by the Swiss National Science Foundation. The research was also partially supported by the NanoSys project, within the program ERC-2009-AdG-246810.
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