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Hybrid Segmentation of Anatomical Data

Imielinska, Celina Z.; Metaxes, Dimitris; Udupa, Jayaram; Jin, Yinpeng; Chen, Ting

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 NLM funded effort to provide a fully implemented and tested Visible Human Project Segmentation and Registration Toolkit (Insight).


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Also Published In

Medical image computing and computer-assisted intervention - MICCAI 2001

More About This Work

Academic Units
Biomedical Informatics
Biomedical Engineering
Lecture Notes in Computer Science, 2208
Published Here
September 29, 2014
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