EBImage—an R package for image processing with applications to cellular phenotypes

By Grégoire Pau, Florian Fuchs, O. Sklyar, Michael Boutros, Wolfgang Huber. Bioinform., 2010

722 citations recorded by the source corpus.

Summary: EBImage provides general purpose functionality for reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. Availability: EBImage is free and open source, released under the LGPL license and available from the Bioconductor project (http://www.bioconductor.org/packages/release/bioc/html/EBImage.html). Contact: gregoire.pau@ebi.ac.uk

Subfields: Biophysics, Media Technology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c7ba786c84c9c5161604e021551a23f098028eba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Shantanu Singh
2. Anne E Carpenter
3. Juan C. Caicedo
4. Oren Z. Kraus
5. Daniel Gerlich
6. Christoph Sommer
7. Hanchuan Peng
8. Kevin W. Eliceiri

In Media Technology

1. Shantanu Singh
2. Juan C. Caicedo
3. Oren Z. Kraus
4. Anne E Carpenter
5. Hanchuan Peng
6. Daniel Gerlich
7. Christoph Sommer
8. Kevin W. Eliceiri

In Molecular Biology

1. Anne E Carpenter
2. Shantanu Singh
3. Juan C. Caicedo
4. Oren Z. Kraus
5. Hanchuan Peng
6. Daniel Gerlich
7. Christoph Sommer
8. Kevin W. Eliceiri

Citations in the corpus

• Machine learning in cell biology – teaching computers to recognize phenotypes (2013, methodology): “Supervised machine learning has been an important backbone for analysis pipelines in many high-content screening projects (Kittler et al., 2007; Fuchs et al., 2010; Neumann et al., 2010; Schmitz et al., 2010; Mercer et al., 2012).”
• Biological Imaging Software Tools (2012, methodology): “…http://hci. iwr.uni-heidelberg.de/vigra/), a computer vision library with an emphasis on customizable algorithms and data structures, and EBImage 39 , a modular package that leverages the R environment (http://www.r-project.org/) to segment cells and extract quantitative cellular descriptors.”
• Data-analysis strategies for image-based cell profiling (2017, methodology): “Although errors”