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Proceedings Paper

Probabilistic multiscale image segmentation: set-up and first results (Proceedings Only)
Author(s): Koen L. Vincken; Andre S.E. Koster; Max A. Viergever
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Paper Abstract

We have developed a method to segment two- and three-dimensional images using a multiscale (hyperstack) approach with probabilistic linking. A hyperstack is a voxel-based multiscale data structure containing linkages between voxels at different scales. The scale-space is constructed by repeatedly applying a discrete convolution with a Gaussian kernel to the original input image. Between these levels of increasing scale we establish child-parent linkages according to a linkage scheme that is based on affection. In the resulting tree-like data structure roots are formed to indicate the most plausible locations in scale-space where objects (of different sizes) are actually defined by a single voxel. Tracing the linkages back from every root to the ground level produces a segmented image. The present paper deals with probabilistic linking, i.e., a set-up in which a child voxel can be linked to more than one parent voxel. The output of the thus constructed hyperstack -- a list of object probabilities per voxel -- can be directly related to the opacities used in volume renderers.

Paper Details

Date Published: 22 September 1992
PDF: 15 pages
Proc. SPIE 1808, Visualization in Biomedical Computing '92, (22 September 1992); doi: 10.1117/12.131068
Show Author Affiliations
Koen L. Vincken, Univ. Hospital Utrecht (Netherlands)
Andre S.E. Koster, Univ. Hospital Utrecht (Netherlands)
Max A. Viergever, Univ. Hospital Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 1808:
Visualization in Biomedical Computing '92
Richard A. Robb, Editor(s)

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