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Proceedings Paper

A novel hybrid model for deformable image registration in abdominal procedures
Author(s): Xishi Huang; Paul S. Babyn; Thomas Looi; Peter C. W. Kim
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Paper Abstract

We propose a novel neuro-fuzzy hybrid transformation model for deformable image registration in intra-operative image guided procedures involving large soft tissue deformation. The hybrid model consists of two parts: a physics-based model and a mathematical approximation model. The physics-based model is based on elastic solid mechanics to model major deformation patterns of the central part of organs, and the mathematical approximation model depicts the deformation of the residual part along organ boundary. A neuro-fuzzy technique is employed to seamlessly integrate the two parts into a unified hybrid model. Its unique feature is to incorporate domain knowledge of soft tissue deformation patterns and significantly reduce the number of transformation parameters. We demonstrate the effectiveness of our hybrid model to register liver magnetic resonance (MR) images in human subject study. This technique has the potential to significantly improve intra-operative image guidance in abdominal and thoracic procedures.

Paper Details

Date Published: 1 March 2011
PDF: 7 pages
Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79640K (1 March 2011); doi: 10.1117/12.878068
Show Author Affiliations
Xishi Huang, The Hospital for Sick Children (Canada)
Univ. of Toronto (Canada)
Paul S. Babyn, Royal Univ. Hospital (Canada)
Thomas Looi, The Hospital for Sick Children (Canada)
Peter C. W. Kim, The Hospital for Sick Children (Canada)
Univ. of Toronto (Canada)


Published in SPIE Proceedings Vol. 7964:
Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; David R. Holmes, Editor(s)

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