| name | bioconductor-ebimage |
| description | EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. Thi |
| when_to_use | Use when: General Image Manipulation: Reading, writing, and displaying multi-dimensional images (JPEG, PNG, TIFF) using readImage, writeImage, and display.; Spatial Transformations: Applying geometric transformations such as translate, rotate, resize, flip, flop, and affine to image arrays.; Image Filtering: Removing noise or detecting edges using linear filters (filter2, gblur) or non-linear filters (media. Not for: Interactive Whole-Slide Analysis: For interactive, manual annotation of large whole-slide tissue images, use QuPath instead because EBImage is designed for programmatic, batch-oriented processing.; Advanced 3D/4D Rendering: For complex 3D volumetric |
| user-invocable | false |
EBImage
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 4.54.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Imports: BiocGenerics, abind, tiff, jpeg, png, locfit, fftwtools, htmltools, htmlwidgets, RCurl
- Install:
BiocManager::install("EBImage")
When to Use
- General Image Manipulation: Reading, writing, and displaying multi-dimensional images (JPEG, PNG, TIFF) using
readImage, writeImage, and display.
- Spatial Transformations: Applying geometric transformations such as
translate, rotate, resize, flip, flop, and affine to image arrays.
- Image Filtering: Removing noise or detecting edges using linear filters (
filter2, gblur) or non-linear filters (medianFilter).
- Cell Segmentation: Segmenting non-touching objects with
bwlabel or separating touching cells using distmap, watershed, and propagate (Voronoi tessellation).
When NOT to Use
- Interactive Whole-Slide Analysis: For interactive, manual annotation of large whole-slide tissue images, use
QuPath instead because EBImage is designed for programmatic, batch-oriented processing.
- Advanced 3D/4D Rendering: For complex 3D volumetric rendering, use
ImageJ/Fiji instead because EBImage's visualization (display) is primarily 2D (browser or raster).
- Deep Learning Segmentation: For out-of-the-box deep learning-based segmentation, use Python packages like
cellpose because EBImage relies on classical intensity-based and morphological segmentation algorithms.
Data Requirements
- Input Format: Image files (JPEG, PNG, TIFF) or numeric arrays.
- Structure:
Image class objects extending the R base class array. Supports multi-dimensional data (e.g., color channels, z-positions, time points).
- Normalization State: Pixel intensities are typically represented as numeric values ranging from 0 to 1.
Key Parameters
- method ("browser"): Specifies how the image is visualized in
display (either "browser" for a JavaScript viewer or "raster" for R's built-in plotting).
- colorMode (Grayscale): Determines how the third and higher dimensions of the image array are rendered (Grayscale or Color).
- sigma (5): Defines the width of the Gaussian filter in
makeBrush or gblur.
- offset (0.05): The threshold offset used in adaptive thresholding (
thresh) to separate foreground from background.
- lambda (100): Controls the relative weighting between sideways and vertical movement in Voronoi tessellation via
propagate.
- w (15): The width of the rectangular box used for adaptive thresholding in
thresh.
Best Practices
- Raster Display: Use
display with method="raster" to combine image data with R's built-in plotting facilities (e.g., adding text labels).
- Noise Reduction: Apply a low-pass filter like
gblur before thresholding to smooth out high-frequency noise and prevent over-segmentation.
- Watershed Seeding: Use
distmap to generate a distance map from a binary image before applying the watershed transformation to separate touching objects.
- Visualization of Segmentation: Use
colorLabels to visualize segmentation results by color-coding objects with a random permutation of unique colors.
Common Pitfalls
- Touching Cells Merged: Cells close to each other are merged into a single object after thresholding. Fix: Use the
watershed algorithm on the distance map (distmap) to separate them.
- Color Channels Displayed as Grayscale: Color images display as separate grayscale frames. Fix: Change the
colorMode of the image to Color using colorMode().
- Small Holes in Foreground: Thresholding leaves small holes inside foreground objects. Fix: Apply the
fillHull function or morphological closing to fill small holes.
Alternatives
- magick: Better for general-purpose, non-biological image manipulation and format conversion, but lacks biological segmentation tools like
watershed.
- imager: An R package based on CImg, excellent for general image processing but has fewer biology-specific features compared to EBImage.
- RBioFormats: Use this if you need to read proprietary microscopy image data and metadata not natively supported by
readImage.
Citations
- Pau, G., Fuchs, F., Steffy, O., Boutros, M., & Huber, W. (2010). EBImage—an R package for image processing with applications on cellular assays. Bioinformatics, 26(7), 979-981.
References
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