Abstract
We present a novel statistical image-match model for use in Bayesian segmentation, a multiscale extension of image profile models akin to those in Active Shape Models. A spherical-harmonic based 3D shape representation provides a mapping of the object boundary to the sphere S 2, and a scale-space for profiles on the sphere defines a scale-space on the object. A key feature is that profiles are not blurred across the object boundary, but only along the boundary. This profile scale-space is sampled in a coarse-to-fine fashion to produce features for the statistical image-match model. A framework for model-building and segmentation has been built, and testing and validation are in progress with a dataset of 70 segmented images of the caudate nucleus.
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© 2004 Springer-Verlag Berlin Heidelberg
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Ho, S., Gerig, G. (2004). Profile Scale-Spaces for Multiscale Image Match. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30135-6_22
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DOI: https://doi.org/10.1007/978-3-540-30135-6_22
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