A computational image analysis glossary for biologists
Abstract
Recent advances in biological imaging have resulted in an explosion in the quality and quantity of images obtained in a digital format. Developmental biologists are increasingly acquiring beautiful and complex images, thus creating vast image datasets. In the past, patterns in image data have been detected by the human eye. Larger datasets, however, necessitate high-throughput objective analysis tools to computationally extract quantitative information from the images. These tools have been developed in collaborations between biologists, computer scientists, mathematicians and physicists. In this Primer we present a glossary of image analysis terms to aid biologists and briefly discuss the importance of robust image analysis in developmental studies.
Additional Information
© 2012 The Company of Biologists Ltd. We thank three anonymous reviewers, Kaoru Sugimoto, Xiaolan Zhang, Yun Zhou, Jill Harrison, Cory Tobin, Erich Schwarz, Henrik Jönsson, Tigran Bacarian, Eric Mjolsness, Heather Meyer and Marcus Heisler for discussions and comments on the manuscript. Funding: We acknowledge the Department of Energy Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences, Office of Basic Energy Sciences of the U.S. Department of Energy (E.M.M.) for funding the experimental research that has provided the images of sepals that are used in the examples given, the Gordon and Betty Moore Foundation Cell Center at Caltech (A.H.K.R. and A.C.) for funding the computational image processing of those images, and a Helen Hay Whitney Foundation postdoctoral fellowship to A.H.K.R.Attached Files
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Additional details
- Eprint ID
- 34527
- Resolver ID
- CaltechAUTHORS:20120927-145601214
- Department of Energy (DOE)
- Gordon and Betty Moore Foundation
- Helen Hay Whitney Foundation
- Created
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2012-09-27Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field