Glossary

Key Points

Automatic thresholding
Batch exploration of segmentation results
Big image data formats
Cloud based batch analysis
Cloud based interactive analysis
Connected component labeling
Convolutional filters
Correlative image rendering
Data types
Digital image basics
Distance transform
Fluorescence microscopy image formation
Global background correction
Image data formats
Local background correction
Lookup tables
  • LUT stands for “look-up table”; it defines how numeric pixel values are mapped to colors for display.

  • A LUT has configurable contrast limits that determine the pixel value range that is rendered linearly.

  • LUT settings must be responsibly chosen to convey the intended scientific message and not to hide relevant information.

  • A gray scale LUT is usually preferable over a colour LUT, especially blue and red are not well visible for many people.

  • For high dynamic range images multi-color LUTs may be useful to visualise a wider range of pixel values.

Median filter
Morphological filters
Multichannel images
N-dimensional images
Neighborhood filters
Object filtering
Object intensity measurements
Object shape measurements
OME-TIFF
OME-Zarr
Projections
Segmentation overview
Skeletonization
Smart microscopy targeted imaging
Spatial calibration
Statistical (rank) filters
Thresholding
Tool installation
Volume rendering
Volume slicing
Watershed
  • A watershed transform can separate touching objects if there are intensity valleys (or ridges) between touching objects. In case of intensity ridges the image needs to be inverted before being subjected to the watershed transform.

  • To separate object by their shape, use a distance transform on the binary image and inject this into the watershed transform. It is often good to smooth the distance transform to remove spurious minima, which could serve as wrong seed points and thus lead to an over-segmentation.

Batch processing
Coding with LLMs
Functions
Loops
Output saving
Recording a script
Running a script
Strings and paths
Variables
Noisy object segmentation and filtering in 2D
Nuclei and cells segmentation
Nuclei segmentation and shape measurement
Quantitative image inspection and presentation
Bioimage tools containers
Deep learning instance segmentation
Image registration (DRAFT)
Manual segmentation
Remote (image) data access
Similarity transformations
Table file formats (DRAFT)
Template
Commenting
Fetching user input
Setting up a scripting environment
Cofilin rod formation (DRAFT)
Segment Golgi objects per cell

Glossary

FIXME