Probabilistic multiscale image segmentation: set-up and first results (Proceedings Only)

Koen L. Vincken; Andre S.E. Koster; Max A. Viergever

doi:10.1117/12.131068

22 September 1992 Probabilistic multiscale image segmentation: set-up and first results (Proceedings Only)

Koen L. Vincken, Andre S.E. Koster, Max A. Viergever

Proceedings Volume 1808, Visualization in Biomedical Computing '92; (1992) https://doi.org/10.1117/12.131068
Event: Visualization in Biomedical Computing, 1992, Chapel Hill, NC, United States

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.

Citation Download Citation

Koen L. Vincken, Andre S.E. Koster, and Max A. Viergever "Probabilistic multiscale image segmentation: set-up and first results (Proceedings Only)", Proc. SPIE 1808, Visualization in Biomedical Computing '92, (22 September 1992); https://doi.org/10.1117/12.131068

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