Journal article
Fuzzy Object Skeletonization: Theory, Algorithms, and Applications
IEEE transactions on visualization and computer graphics, Vol.24(8), pp.2298-2314
08/01/2018
DOI: 10.1109/TVCG.2017.2738023
PMID: 28809701
Abstract
Skeletonization offers a compact representation of an object while preserving important topological and geometrical features. Literature on skeletonization of binary objects is quite mature. However, challenges involved with skeletonization of fuzzy objects are mostly unanswered. This paper presents a new theory and algorithm of skeletonization for fuzzy objects, evaluates its performance, and demonstrates its applications. A formulation of fuzzy grassfire propagation is introduced; its relationships with fuzzy distance functions, level sets, and geodesics are discussed; and several new theoretical results are presented in the continuous space. A notion of collision-impact of fire-fronts at skeletal points is introduced, and its role in filtering noisy skeletal points is demonstrated. A fuzzy object skeletonization algorithm is developed using new notions of surface- and curve-skeletal voxels, digital collision-impact, filtering of noisy skeletal voxels, and continuity of skeletal surfaces. A skeletal noise pruning algorithm is presented using branch-level significance. Accuracy and robustness of the new algorithm are examined on computer-generated phantoms and micro- and conventional CT imaging of trabecular bone specimens. An application of fuzzy object skeletonization to compute structure-width at a low image resolution is demonstrated, and its ability to predict bone strength is examined. Finally, the performance of the new fuzzy object skeletonization algorithm is compared with two binary object skeletonization methods.
Details
- Title: Subtitle
- Fuzzy Object Skeletonization: Theory, Algorithms, and Applications
- Creators
- Punam K Saha - University of IowaDakai Jin - University of IowaYinxiao Liu - University of IowaGary E Christensen - University of IowaCheng Chen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on visualization and computer graphics, Vol.24(8), pp.2298-2314
- Publisher
- IEEE
- DOI
- 10.1109/TVCG.2017.2738023
- PMID
- 28809701
- ISSN
- 1077-2626
- eISSN
- 1941-0506
- Grant note
- R01-AR054439 / NIH
- Language
- English
- Date published
- 08/01/2018
- Academic Unit
- Radiology; Electrical and Computer Engineering; Radiation Oncology; Radiation Research Laboratory; The Iowa Institute for Biomedical Imaging; Advanced Pulmonary Physiomic Imaging Laboratory; Holden Comprehensive Cancer Center
- Record Identifier
- 9984197259502771
Metrics
16 Record Views