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Related Experiment Video

Updated: May 29, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Distance transform for images represented by quadtrees.

H Samet1

  • 1Department of Computer Science, University of Maryland, College Park, MD 20742.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study adapts image processing distance metrics to quadtree representations, introducing a chessboard distance transform. This new method efficiently calculates distances from black nodes to white nodes in quadtrees.

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Area of Science:

  • Computer Science
  • Image Processing
  • Computational Geometry

Background:

  • Traditional image processing often uses binary array representations for distance calculations.
  • Quadtrees offer a hierarchical data structure for image representation, but adapting distance metrics can be challenging.

Purpose of the Study:

  • To adapt the concept of distance from binary array image representations to quadtree representations.
  • To define and compute a chessboard distance transform for quadtrees.

Main Methods:

  • The chessboard distance metric was adapted for quadtree structures.
  • A novel algorithm was developed to compute the chessboard distance transform by examining black nodes and their neighbors (including diagonal).
  • The algorithm's efficiency was analyzed in relation to the number of leaf nodes.

Main Results:

  • The chessboard distance metric is suitable for quadtree representations.
  • A chessboard distance transform for quadtrees was defined as the minimum distance from black nodes to the border of white nodes.
  • The presented algorithm efficiently computes this transform, requiring diagonal neighbor identification.

Conclusions:

  • The chessboard distance transform provides a new method for analyzing spatial relationships in quadtree-represented images.
  • The algorithm offers an efficient approach to computing this transform, with performance tied to the quadtree's leaf node count.
  • This work extends distance transform concepts to hierarchical image data structures.