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A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging

  • Dakai Jin
  • Punam K. Saha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Skeletonization provides a simple yet compact representation of an object and is widely used in medical imaging applications including volumetric, structural, and topological analyses, object representation, stenoses detection, path-finding etc. Literature of three-dimensional skeletonization is quite matured for binary digital objects. However, the challenges of skeletonization for fuzzy objects are mostly unanswered. Here, a framework and an algorithm for fuzzy surface skeletonization are developed using a notion of fuzzy grassfire propagation which will minimize binarization related data loss. Several concepts including fuzzy axial voxels, local and global significance factors are introduced. A skeletal noise pruning algorithm using global significance factors as significance measures of individual branches is developed. Results of application of the algorithm on several medical objects have been illustrated. A quantitative comparison with an ideal skeleton has demonstrated that the algorithm can achieve sub-voxel accuracies at various levels of noise and downsampling. The role of fuzzy skeletonization in thickness computation at relatively low resolution has been demonstrated.

Keywords

Fuzzy Object Fire Front Thickness Computation Skeletonization Algorithm Binary Object 
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 2013

Authors and Affiliations

  • Dakai Jin
    • 1
  • Punam K. Saha
    • 1
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of IowaUSA
  2. 2.Department of RadiologyUniversity of IowaUSA

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