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Proceedings Paper

Multiscale shape description of MR brain images using active contour models
Author(s): Julia Anne Schnabel; Simon Robert Arridge
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Paper Abstract

In this paper we present a hierarchical multiscale shape description tool based on active contour models which enables data-driven quantitative and qualitative shape studies of MR brain images at multiple scales. At large scales, global shape properties are extracted from the image, while smaller scale features are suppressed. At lower scales, the detailed shape characteristics become more prominent. Extracting a shape at different levels of scale yields a hierarchical multiscale shape stack. This shape stack can be used to localize and characterize shape changes like deformations and abnormalities at different levels of scale. The shape description is performed as a set of implicit segmentation steps at multiple scales yielding descriptions of an object at various levels of detail. Implicit segmentation is carried out using the well-known model of active contours. Starting from an initial active contour, several implicit optimization processes with differently regularized energy functions are performed, where the energy functions are represented as functions of scale. The presented algorithm for shape focusing and description based on active contour models shows promising results on extracting and characterizing complex shapes in MR brain images at a large set of scales.

Paper Details

Date Published: 16 April 1996
PDF: 11 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237962
Show Author Affiliations
Julia Anne Schnabel, Univ. College London (United Kingdom)
Simon Robert Arridge, Univ. College London (United Kingdom)

Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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