
Proceedings Paper
Multi-scale shape prior using wavelet packet representation and independent component analysisFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
Statistical shape priors try to faithfully represent the full range of biological variations in anatomical structures. These
priors are now widely used to restrict shapes; obtained in applications like segmentation and registration; to a subspace
of plausible shapes. Principle component analysis (PCA) is commonly used to represent modes of shape variations in a
training set. In an attempt to face some of the limitations in the PCA-based shape model, this paper describes a new
multi-scale shape prior using independent component analysis (ICA) and adaptive wavelet decomposition. Within a
best basis selection framework, the proposed method benefits from the multi-scale nature of wavelet packets, and the
capability of ICA to capture higher order statistics in wavelet subspaces. The proposed approach is evaluated using
contours from digital x-ray images of five vertebrae of human spine. We demonstrate the ability of the proposed shape
prior to capture both local and global shape variations, even with limited number of training samples. Our results also
show the performance gains of the ICA-based analysis for the wavelet sub-spaces, as compared to PCA-based analysis
approach.
Paper Details
Date Published: 26 March 2007
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651210 (26 March 2007); doi: 10.1117/12.710298
Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)
PDF: 8 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651210 (26 March 2007); doi: 10.1117/12.710298
Show Author Affiliations
Nelson Durdle, Univ. of Alberta (Canada)
Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)
© SPIE. Terms of Use
