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Related Experiment Video

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Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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Multithreaded two-pass connected components labelling and particle analysis in ImageJ.

Michael Doube1

  • 1Department of Infectious Diseases and Public Health, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR.

Royal Society Open Science
|May 7, 2021
PubMed
Summary
This summary is machine-generated.

A new multithreaded connected components labelling algorithm offers linear scaling and significantly faster image segmentation. This efficient implementation, featuring a novel

Keywords:
connected components labellingparallel processingsequential region labelling

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Area of Science:

  • Image analysis
  • Computational biology
  • Scientific computing

Background:

  • Connected components labelling is crucial for image segmentation in various scientific fields.
  • Existing methods can be a bottleneck in image processing pipelines.
  • Efficient algorithms are needed for large-scale image analysis.

Purpose of the Study:

  • To develop a faster, multithreaded implementation of sequential region labelling.
  • To introduce an efficient collision resolution step ('bucket fountain').
  • To validate and assess the performance and scaling of the new implementation.

Main Methods:

  • Implementation of a classical two-pass sequential region labelling algorithm.
  • Integration of a novel 'bucket fountain' collision resolution technique.
  • Performance testing on diverse image sizes and varying CPU thread counts.

Main Results:

  • The new implementation demonstrates linear scaling (O(n)) with image size.
  • Significant speed improvements were observed, reducing processing time from hours to seconds for large images.
  • Performance scales effectively with multithreading up to 8-16 CPU threads, indicating memory bandwidth limitations.

Conclusions:

  • The developed multithreaded connected components labelling algorithm provides a substantial performance improvement.
  • The 'bucket fountain' method enhances efficiency in region labelling.
  • This open-source implementation, available in BoneJ, addresses a key bottleneck in scientific image processing.