Abstract
Groupwise non-rigid registration is an important technique in medical image analysis. Recent studies show that its accuracy can be greatly improved by explicitly providing good initialisation. This is achieved by seeking a sparse correspondence using a parts+geometry model. In this paper we show that a single parts+geometry model is unlikely to establish consistent sparse correspondence for complex objects, and that better initialisation can be achieved using a set of models. We describe how to combine the strengths of multiple models, and demonstrate that the method gives state-of-the-art performance on three datasets, with the most significant improvement on the most challenging.
Keywords
- Reference Image
- Geometry Model
- Dense Point
- Multiple Part
- Good Initialisation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zhang, P., Yap, PT., Shen, D., Cootes, T.F. (2012). Initialising Groupwise Non-rigid Registration Using Multiple Parts+Geometry Models. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_20
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DOI: https://doi.org/10.1007/978-3-642-33454-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33453-5
Online ISBN: 978-3-642-33454-2
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