DEVILS

DEVILS enhances visualization of large high-dynamic-range image stacks in ImageJ to reveal both low- and high-intensity features for microscopy, medical imaging, and volumetric dataset inspection.


Key Features:

  • Dynamic Range Compression: Compresses the dynamic range of image-based sensor data to present high- and low-intensity features simultaneously and to match display capabilities and human perceptual limits.
  • Local and Global Pixel Operations: Employs combined local contrast enhancement and global intensity normalization to homogenize intensities and compensate for non-homogeneous staining efficiencies and depth-dependent light penetration.
  • Parallel Processing: Processes multiple image planes in parallel to accelerate handling of large volumetric datasets.
  • BigDataViewer Compatibility: Outputs processed data in a format compatible with BigDataViewer for fast visualization of results.

Scientific Applications:

  • Microscopy: Reveals dim subcellular structures and bright features concurrently across volumetric microscopy stacks.
  • Medical Imaging: Enables visual inspection of features spanning wide intensity ranges in medical imaging datasets.
  • Biological Research and Diagnostics: Supports analysis of specimens with uneven staining or variable light penetration by making both subtle and prominent structures visible.
  • Materials Science: Facilitates inspection of volumetric materials datasets where features of interest occur across a broad dynamic range.

Methodology:

Applies dynamic range compression together with local pixel operations for contrast enhancement and global intensity normalization, performs parallel processing across image planes, and writes output in a BigDataViewer-compatible format.

Topics

Details

License:
GPL-3.0
Tool Type:
plugin
Programming Languages:
Java
Added:
9/8/2021
Last Updated:
9/13/2021

Operations

Publications

Guiet R, Burri O, Chiaruttini N, Hagens O, Seitz A. DEVILS: a tool for the visualization of large datasets with a high dynamic range. F1000Research. 2021;9:1380. doi:10.12688/f1000research.25447.2. PMID:33976878. PMCID:PMC8097733.