Seeing is believing: Quantifying is convincing: Computational image analysis in biology
Research output: Contribution to book/Conference proceedings/Anthology/Report › Chapter in book/Anthology/Report › Contributed › peer-review
Contributors
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 tissuelevel imaging, providing pointers to the specialized literature and listing available software tools. We place particular emphasis on clear categorization of imageanalysis 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.
Details
Original language | English |
---|---|
Title of host publication | Focus on Bio-Image Informatics |
Editors | Winnok H. De Vos, Sebastian Munck, Jean-Pierre Timmermans |
Publisher | Springer Science and Business Media B.V. |
Pages | 1-39 |
Number of pages | 39 |
ISBN (electronic) | 978-3-319-28549-8 |
ISBN (print) | 978-3-319-28547-4 |
Publication status | Published - 21 May 2016 |
Peer-reviewed | Yes |
Publication series
Series | Advances in Anatomy Embryology and Cell Biology |
---|---|
Volume | 219 |
ISSN | 0301-5556 |
External IDs
Scopus | 84976407823 |
---|---|
ORCID | /0000-0003-4414-4340/work/142252136 |
Keywords
Keywords
- Point Spread Function, Markov Random Field, Imaging Model, Uncertainty Quantification, Evidence Theory