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Proceedings Paper

Multiresolution segmentation of medical images using shape-restricted snakes
Author(s): Christian Juan Knoll; Mariano Luis Alcaniz-Raya; Carlos Monserrat; Vincente Grau Colomer; M. Carmen Juan
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Paper Abstract

We propose a new technique for elastic deformation restriction of active contour models to particular object shapes. For this purpose we apply localized multi-scale contour parametrization based on the 1D dyadic Wavelet Transform (WT) as a multi-scale boundary curve analysis tool. Our approach determines the WT-coefficients within a certain scale range, which differ significantly from the correspondent WT-coefficients of the most similar model in a training set. Those WT-coefficients are replaced by the correspondent model WT-coefficients to perform the reconstruction of the contour. The difference of the original deformed contour and the reconstructed contour is used as inner snake forces. By this technique it can be avoided, that the deformable contour is trapped into disturbing local minima of the snakes potential due to noise or irrelevant image features. The contour deformation method is integrated in a coarse to fine segmentation frame based on a multiscale image edge representation using the local modulus maxima of the dyadic Wavelet Transform. For detection of the object's position and initialization of the snake we apply a multiresolution binary matched filter at a coarse scale containing few detail information.

Paper Details

Date Published: 21 May 1999
PDF: 12 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348577
Show Author Affiliations
Christian Juan Knoll, Univ. Politecnica de Valencia and Technische Univ. Erlangen-Nuernberg (Spain)
Mariano Luis Alcaniz-Raya, Univ. Politecnica de Valencia (Spain)
Carlos Monserrat, Univ. Politecnica de Valencia (Spain)
Vincente Grau Colomer, Univ. Politecnica de Valencia (Spain)
M. Carmen Juan, Univ. Politecnica de Valencia (Spain)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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