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Proceedings Paper

Shape-based models for interactive segmentation of medical images
Author(s): Kevin P. Hinshaw; Russ B. Altman; James F. Brinkley M.D.
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Paper Abstract

Accurate image segmentation is one of the key problems in computer vision. In domains such as radiation treatment planning, dosimetrists must manually trace the outlines of a few critical structures on large numbers of images. Considerable similarity can be seen in the shape of these regions, both between adjacent slices in a particular patient and across the spectrum of patients. Consequently we should be able to model this similarity and use it to assist in the process of segmentation. Previous work has demonstrated that a constraint-based 2D radial model can capture generic shape information for certain shape classes, and can reduce user interaction by a factor of three over purely manual segmentation. Additional simulation studies have shown that a probabilistic version of the model has the potential to further reduce user interaction. This paper describes an implementation of both models in a general-purpose imaging and graphics framework and compares the usefulness of the models on several shape classes.

Paper Details

Date Published: 12 May 1995
PDF: 10 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208750
Show Author Affiliations
Kevin P. Hinshaw, Univ. of Washington (United States)
Russ B. Altman, Stanford Univ. School of Medicine (United States)
James F. Brinkley M.D., Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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