Abstract
Bioimaging, image segmentation, thresholding, and multivariate processing are helpful tools in analysis of series of images from many time lapse experiments. The different methods of mathematic, algorithmization and artificial intelligence could by modified, parametrized or adopted for single purpose case of completely different biological background (namely microorganisms, tissue cultures, aquaculture). However, most of the task is based on initial image segmentation, before features axtraction and comparison tasks are evaluated. In this article, we compare several of classical approaches in bioinformatical and biophysical cases with the neural network approach. The concept of neural network was adopted from the biological neural networks. Th networks need to be trained, however after the learning phase, they should be able to find one solution for various objects. The comparison of the methods is evaluated via error in segmentation according to the human supervisor.
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Acknowledgement
This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic - projects ‘CENAKVA’ (No. CZ.1.05/2.1.00/01.0024) and ‘CENAKVA II’ (No. LO1205 under the NPU I program) and FAV.
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Urbanová, P., Vaněk, J., Souček, P., Štys, D., Císař, P., Železný, M. (2017). Bioimaging - Autothresholding and Segmentation via Neural Networks. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_31
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DOI: https://doi.org/10.1007/978-3-319-56148-6_31
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