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Seeing Is Believing: Quantifying Is Convincing: Computational Image Analysis in Biology

  • Ivo F. Sbalzarini
Chapter
Part of the Advances in Anatomy, Embryology and Cell Biology book series (ADVSANAT, volume 219)

Abstract

Imaging is center stage in biology. Advances in microscopy and labeling techniques have enabled unprecedented observations and continue to inspire new developments. Efficient and accurate quantification and computational analysis of the acquired images, however, are becoming the bottleneck. We review different paradigms of computational image analysis for intracellular, single-cell, and tissue-level imaging, providing pointers to the specialized literature and listing available software tools. We place particular emphasis on clear categorization of image-analysis frameworks and on identifying current trends and challenges in the field. We further outline some of the methodological advances that are required in order to use images as quantitative scientific measurements.

Keywords

Point Spread Function Markov Random Field Imaging Model Uncertainty Quantification Evidence Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

I thank all members of the MOSAIC Group for their creative and scientific contributions and for providing multiple test images and illustration cases for this manuscript. Particular thanks go to Dr. Grégory Paul, Dr. Janick Cardinale, and Dr. Jo Helmuth. This work was supported in parts by the German Federal Ministry of Research and Education (BMBF) under funding code 031A099.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Scientific Computing for Systems Biology, Faculty of Computer ScienceTU DresdenDresdenGermany
  2. 2.MOSAIC Group, Center for Systems Biology DresdenDresdenGermany
  3. 3.Max Planck Institute of Molecular Cell Biology and GeneticsDresdenGermany

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