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Proceedings Paper

Fuzzy-connected 3D image segmentation at interactive speeds
Author(s): Laszlo G. Nyul; Alexandre Xavier Falcao; Jayaram K. Udupa
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Paper Abstract

Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem with these algorithms has been their excessive computational requirements. In an attempt to substantially speed them up, in the present paper, we study systematically a host of 18 algorithms under two categories -- label correcting and label setting. Extensive testing of these algorithms on a variety of 3D medical images taken from large ongoing applications demonstrates that a 20 - 360 fold improvement over current speeds is achievable with a combination of algorithms and fast modern PCs. The reliable recognition (assisted by human operators) and the accurate, efficient, and sophisticated delineation (automatically performed by the computer) can be effectively incorporated into a single interactive process. If images having intensities with tissue specific meaning (such as CT or standardized MR images) are utilized, all parameters for the segmentation method can be fixed once for all, all intermediate data can be computed before the user interaction is needed, and the user can be provided with more information at the time of interaction.

Paper Details

Date Published: 6 June 2000
PDF: 12 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387681
Show Author Affiliations
Laszlo G. Nyul, Univ. of Pennsylvania (Hungary)
Alexandre Xavier Falcao, State Univ. of Campinas (Brazil)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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