Image Enhancement with Deep Learning and Intensity Transformation of No-Reference Grayscale Fluorescence Microscopy Images-Reference-Cited by-同舟云学术

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Image Enhancement with Deep Learning and Intensity Transformation of No-Reference Grayscale Fluorescence Microscopy Images

Published:2023 Issue: Volume: Page:401-410
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
Short-container-title:

Author:

Kakumani Aruna Kumari,Sree L Padma

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-0601-7_31

Reference21 articles.

1. Meijering E, Dzyubachyk O, Smal I (2012) Methods for cell and particle tracking. Methods Enzymol 504(February):183–200

2. Panteli A, Gupta DK, De Bruijn N (2020) Siamese Tracking of Cell Behaviour Patterns Efstratios Gavves. Proc Mach Learn Res [Internet]. 121:570–587

3. Al-Kofahi Y, Zaltsman A, Graves R, Marshall W, Rusu M (2018) A deep learning-based algorithm for 2-D cell segmentation in microscopy images. BMC Bioinformatics 19(1):1–11

4. Das DK, Maiti AK, Chakraborty C (2015) Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J Microsc 257(3):238–252

5. Harder N, Mora-Bermúdez F, Godinez WJ, Wünsche A, Eils R, Ellenberg J et al (2009) Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time. Genome Res 19(11):2113–2124

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