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Proceedings Paper

Medical anatomy segmentation kit: combining 2D and 3D segmentation methods to enhance functionality
Author(s): Gregg S. Tracton; Edward L. Chaney; Julian G. Rosenman; Stephen M. Pizer
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Paper Abstract

Image segmentation, in particular, defining normal anatomic structures and diseased or malformed tissue from tomographic images, is common in medical applications. Defining tumors or arterio-venous malformation from computed tomography or magnetic resonance images are typical examples. This paper describes a program, Medical Anatomy Segmentation Kit (MASK), whose design acknowledges that no single segmentation technique has proven to be successful or optimal for all object definition tasks associated with medical images. A practical solution is offered through a suite of complementary user-guided segmentation techniques and extensive manual editing functions to reach the final object definition goal. Manual editing can also be used to define objects which are abstract or otherwise not well represented in the image data and so require direct human definition - e.g., a radiotherapy target volume which requires human knowledge and judgement regarding image interpretation and tumor spread characteristics. Results are either in the form of 2D boundaries or regions of labeled pixels or voxels. MASK currently uses thresholding and edge detection to form contours, and 2D or 3D scale-sensitive fill and region algebra to form regions. In addition to these proven techniques, MASK's architecture anticipates clinically practical automatic 2D and 3D segmentation methods of the future.

Paper Details

Date Published: 8 July 1994
PDF: 12 pages
Proc. SPIE 2299, Mathematical Methods in Medical Imaging III, (8 July 1994); doi: 10.1117/12.179274
Show Author Affiliations
Gregg S. Tracton, Univ. of North Carolina/Chapel Hill (United States)
Edward L. Chaney, Univ. of North Carolina/Chapel Hill (United States)
Julian G. Rosenman, Univ. of North Carolina/Chapel Hill (United States)
Stephen M. Pizer, Univ. of North Carolina/Chapel Hill (United States)


Published in SPIE Proceedings Vol. 2299:
Mathematical Methods in Medical Imaging III
Fred L. Bookstein; James S. Duncan; Nicholas Lange; David C. Wilson, Editor(s)

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