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Schnabel, J.A.* ; Arridge, S.R.*

Multiscale shape description of MR brain images using active contour models.

Proc. SPIE 2710, 596-606 (1996)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Active Contour Models ; Contour Extraction ; Curvature ; Differential Invariants ; Hierarchical Description ; Multiscale Representation ; Shape Analysis ; Snakes
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 2710, Heft: , Seiten: 596-606 Artikelnummer: , Supplement: ,
Verlag SPIE
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)