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Fast, Quality, Segmentation of Large Volumes – Isoperimetric Distance Trees

  • Leo Grady
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

For many medical segmentation tasks, the contrast along most of the boundary of the target object is high, allowing simple thresholding or region growing approaches to provide nearly sufficient solutions for the task. However, the regions recovered by these techniques frequently leak through bottlenecks in which the contrast is low or non-existent. We propose a new approach based on a novel speed-up of the isoperimetric algorithm [1] that can solve the problem of leaks through a bottleneck. The speed enhancement converts the isoperimetric segmentation algorithm to a fast, linear-time computation by using a tree representation as the underlying graph instead of a standard lattice structure. In this paper, we show how to create an appropriate tree substrate for the segmentation problem and how to use this structure to perform a linear-time computation of the isoperimetric algorithm. This approach is shown to overcome common problems with watershed-based techniques and to provide fast, high-quality results on large datasets.

Keywords

Gaussian Elimination Laplacian Matrix Graph Partitioning Cholesky Factor Distance Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leo Grady
    • 1
  1. 1.Department of Imaging and VisualizationSiemens Corporate ResearchPrincetonUSA

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