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Proceedings Paper

Image segmentation using local shape and gray-level appearance models
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Paper Abstract

A new generic model-based segmentation scheme is presented, which can be trained from examples akin to the Active Shape Model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Because in the ASM approach the intensity and shape models are typically applied alternately during optimizing as first an optimal target location is selected for each landmark separately based on local gray-level appearance information only to which the shape model is fitted subsequently, the ASM may be misled in case of wrongly selected landmark locations. Instead, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized non-iteratively using dynamic programming which allows to find the optimal landmark positions using combined shape and intensity information, without the need for initialization.

Paper Details

Date Published: 8 March 2006
PDF: 10 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614401 (8 March 2006); doi: 10.1117/12.648404
Show Author Affiliations
Dieter Seghers D.D.S., Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Dirk Loeckx, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Frederik Maes, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)
Paul Suetens, Katholieke Univ. Leuven, Univ. Hospital Gasthuisberg (Belgium)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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