Abstract
Microscopes have been used for a long time to observe biological samples. However, measurements of tissue and cell-related parameters were conducted by human observers and were consequently ad hoc, not reproducible and restricted to small sample numbers. Since computers have become increasingly powerful, classic life-sciences now routinely take advantage of new opportunities to link microscopes and computers. Automated image-segmentation in large numbers of digital images allows recognition of cell and tissue structures via computer algorithms, and subsequent linear measurements of cellular parameters. Nevertheless, they also come with technical challenges linked to memory, computational resources, disk space and sensor limitations as well as new software algorithm approaches for image processing.
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- 1.
http://www.autopano.net/en/
- 2.
http://hugin.sourceforge.net/
- 3.
http://www.tissuegnostics.com
- 4.
www.cellprofiler.org
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Heindl, A. et al. (2012). Towards the Automated Detection and Characterization of Osteoclasts in Microscopic Images. In: Pietschmann, P. (eds) Principles of Osteoimmunology. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0520-7_2
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DOI: https://doi.org/10.1007/978-3-7091-0520-7_2
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