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Proceedings Paper

Wavelet-enhanced appearance modeling
Author(s): Mikkel B. Stegmann; Soren Forchhammer; Timothy F. Cootes
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Paper Abstract

Generative segmentation methods such as the Active Appearance Models (AAM) establish dense correspondences by modelling variation of shape and pixel intensities. Alas, for 3D and high-resolution 2D images typical in medical imaging, this approach is rendered infeasible due to excessive storage and computational requirements. This paper extends the previous work of Wolstenholme and Taylor where Haar wavelet coefficient subsets were modelled rather than pixel intensities. In addition to a detailed review of the method and a discussion of the integration into an AAM-framework, we demonstrate that the more recent bi-orthogonal CDF 9-7 wavelet offers advantages over the traditional Haar wavelet in terms of synthesis quality and accuracy. Further, we demonstrate that the inherent frequency separation in wavelets allows for simple band-pass filtering, e.g. edge-emphasis. Experiments using Haar and CDF 9-7 wavelets on face images have shown that segmentation accuracy degrades gracefully with increasing compression ratio. Further, a proposed weighting scheme emphasizing edges was shown to be significantly more accurate at compression ratio 1:1, than a conventional AAM. At higher compression ratios the scheme offered both a decrease in complexity and an increase in segmentation accuracy.

Paper Details

Date Published: 12 May 2004
PDF: 10 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535797
Show Author Affiliations
Mikkel B. Stegmann, Technical Univ. of Denmark (Denmark)
Soren Forchhammer, Technical Univ. of Denmark (Denmark)
Timothy F. Cootes, Univ. of Manchester (United Kingdom)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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