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A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging
Book chapter   Open access   Peer reviewed

A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging

Dakai Jin and Punam K Saha
Image Analysis and Processing – ICIAP 2013, pp.662-671
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2013
DOI: 10.1007/978-3-642-41181-6_67
url
https://doi.org/10.1007/978-3-642-41181-6_67View
Published (Version of record) Open Access

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.
 Binary Object Fire Front Fuzzy Object Skeletonization Algorithm Thickness Computation

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