Computer Vision for Microscopy Image Analysis
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AlbakiReads Editorial Computer vision methods for turning microscopy data into interpretable results
Computer vision methods for turning microscopy data into interpretable results
Large-scale microscopy produces more visual data than manual annotation can readily handle. Edited by Mei Chen, this technical volume connects computer vision with biomedical image analysis, emphasizing modern methods including deep learning. Its chapter-by-chapter organization follows major analytical tasks: detection and segmentation, classification, tracking, and event detection. The book frames these tools as part of a collaborative workflow between biologists, who generate and interpret experimental data, and computer vision specialists developing automated approaches. It is centered on the practical challenge of converting high-volume microscopy images into information that can support image-based biomedical research.
Good fit for readers who enjoy
- Computer scientists working on image analysis and computer vision
- Biologists processing microscopy data from image-based experiments
- Researchers interested in deep learning applications for biomedical imaging
Themes
Microscopy image analysisComputer visionDeep learningBiomedical discoveryDetection, segmentation, tracking, and classification
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Book Overview Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based...
Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts.Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information.Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation.This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection.
Book Details Format: Paperback | Pages: 228 | Language: English | Publisher: ACADEMIC PR INC | ISBN: 0128149728
FormatPaperback
Pages228
LanguageEnglish
ISBN0128149728
EAN9780128149720
PublisherACADEMIC PR INC
Publication Date2020-12-04
Edition1
AccessoriesNo Accessory
ConditionNew
Product TypeQUALITY PAPERBACK BOOKS
Weight0.89 Pounds
Length9.25 Inches
Width7.5 Inches
Height0.48 Inches
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