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Proceedings Paper

Probabilistic segmentation using edge detection and region growing
Author(s): Russell R. Stringham; William A. Barrett; David C. Taylor
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Paper Abstract

A new segmentation algorithm is described which incorporates both region and edge information. The algorithm allows simultaneous segmentation of multiple anatomical objects given one or more user-specified disc-shaped seed regions which sample the density characteristics of the underlying anatomy. The algorithm is iterative in nature, using the seed discs to grow out the specified region(s), for the initial image slice, through a type of connected component labeling. The final segmentation from the previous image slice seeds the segmentation for the next adjoining slice until the entire image volume is processed. The algorithm requires no training, is adaptive, demonstrating good performance for differing data types including CT and MRI, and requires minimal user input. The output of the segmentation algorithm is a three-dimensional (3-D) n-ary scene (where n specifies the number of segmented regions) which is amenable to surface rendering, via surface tracking, or volume rendering by masking the n-ary scene against the original image volume.

Paper Details

Date Published: 22 September 1992
PDF: 12 pages
Proc. SPIE 1808, Visualization in Biomedical Computing '92, (22 September 1992); doi: 10.1117/12.131066
Show Author Affiliations
Russell R. Stringham, Brigham Young Univ. (United States)
William A. Barrett, Brigham Young Univ. (United States)
David C. Taylor, Brigham Young Univ. (United States)


Published in SPIE Proceedings Vol. 1808:
Visualization in Biomedical Computing '92

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