Abstract
The study of cell morphology and cell mobility variation when cells are grown on top of patterned substrates is becoming a very important factor in tissue regeneration.
In this paper we present a novel approach to automatically detect and remove periodic background patterns in brightfield microscopy images. This background removal process is fundamental for the analysis of cell mobility as the periodic background pattern would otherwise lead to erroneous cell analysis. The detection of the background is performed by searching for the periodic background pattern organization through the analysis of keypoints automatically obtained from images. Using this information we are able to both detect and reconstruct the periodic background and finally remove it from the original images.
We tested the proposed approach on microscopy images with different periodic background patterns. The effectiveness of the method was validated both by visual inspection and by the cell tracking results obtained.
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Esteves, T., Carvalho, Â., Monteiro, F.J., Quelhas, P. (2014). Periodic Background Pattern Detection and Removal for Cell Tracking. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_14
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DOI: https://doi.org/10.1007/978-3-319-11755-3_14
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