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Updated: Jul 7, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Memory efficient propagation-based watershed and influence zone algorithms for large images.

I Pitas1, C I Cotsaces

  • 1Dept. of Inf., Thessaloniki Univ., Greece. pitas@zeus.csd.auth.gr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 12, 2008
PubMed
Summary
This summary is machine-generated.

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This study introduces memory-efficient algorithms for image processing, specifically for skeletonization by influence zones and watershed transform. These novel methods significantly reduce memory usage for large images, enhancing computational efficiency.

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Propagation front and grassfire methods are widely used in image processing due to their efficiency and geodesic properties.
  • However, their random-access nature limits their applicability to large images that exceed available random access memory.

Purpose of the Study:

  • To enhance the memory efficiency of algorithms utilizing propagation fronts, specifically skeletonization by influence zones and the watershed transform.
  • To develop novel algorithms that reduce memory footprint for processing large-scale image data.

Main Methods:

  • Developed two algorithms for skeletonization by influence zones: one operating on surfaces without storing the enclosing volume, and another using only propagation fronts.
  • Created a watershed transform algorithm that retains only propagation fronts and a single greylevel of the image in memory.

Related Experiment Videos

Last Updated: Jul 7, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

  • Implemented techniques such as fast search methods, double propagation fronts, and directional propagation to minimize set operation effects.

    • Main Results:

    • All three developed algorithms demonstrate substantially reduced memory consumption compared to existing methods.
    • The new algorithms enable efficient processing of large images that previously could not fit into memory.
    • Significant memory savings were achieved for both skeletonization by influence zones and watershed transform.

    Conclusions:

    • The presented algorithms offer a significant improvement in memory efficiency for propagation front-based image processing techniques.
    • These advancements make complex image analysis tasks, such as skeletonization and watershed segmentation, feasible for larger datasets.
    • The work provides practical solutions for overcoming memory limitations in computational image analysis.