Multi-scale shape prior using wavelet packet representation and independent component analysis

Rami Zewail; Ahmed Elsafi; Nelson Durdle

doi:10.1117/12.710298

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.

Citation Download Citation

Rami Zewail, Rami Zewail, Ahmed Elsafi, Ahmed Elsafi, Nelson Durdle, Nelson Durdle, } "Multi-scale shape prior using wavelet packet representation and independent component analysis", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651210 (26 March 2007); doi: 10.1117/12.710298; https://doi.org/10.1117/12.710298

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