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.
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Jin, D., Saha, P.K. (2013). A New Fuzzy Skeletonization Algorithm and Its Applications to Medical Imaging. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_67
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DOI: https://doi.org/10.1007/978-3-642-41181-6_67
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