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16 April 1996 Multiscale shape description of MR brain images using active contour models
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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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Julia Anne Schnabel and Simon Robert Arridge "Multiscale shape description of MR brain images using active contour models", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237962
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