We propose new hybrid methods for automated segmentation of radiological patient data and the Visible Human data. In this paper, we integrate boundary-based and region-based segmentation methods which amplifies the strength but reduces the weakness of both approaches. The novelty comes from combining a boundary-based method, the deformable model-based segmentation with region-based segmentation methods, the fuzzy connectedness and Voronoi Diagram-based segmentation, to develop hybrid methods that yield high precision, accuracy and efficiency. This work is a part of a NTM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight).
CITATION STYLE
Imielinska, C., Metaxas, D., Udupa, J., Jin, Y., & Chen, T. (2001). Hybrid Segmentation of Anatomical Data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2208, pp. 1048–1057). Springer Verlag. https://doi.org/10.1007/3-540-45468-3_125
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