TubeTK: Difference between revisions
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= Overview = | = Overview = | ||
TubeTK is being developed to host algorithms for applications involving images of tubes. By focusing on the | TubeTK is being developed to host algorithms for applications involving images of tubes (blood vessel in medical images, roads in satellite images, etc.). It also offers methods for handling other geometries (points, surfaces, and densities) in images. | ||
By focusing on local geometry structure, the algorithms are able to accomplish segmentations, registrations, and other analyses that consider the physicial properties of objects and their variations, while not requiring limiting assumptions on the specific arrangement or general shape of the objects in the images. We are applying these techniques to push image understanding in new directions such as: | |||
# registration of abdominal images even when organs slides against one another | |||
# forming statistical atlases of intra-canrial vessel network topology even when that topology changes between subjects | |||
# segmentation of arbitrary objects in images even when intensity statistics of those objects, and the objects around them, vary from image to image. | |||
At this time TubeTK is targeted for | |||
# Software developers who wish to write code to integrate our algorithms into their applications | |||
# Researchers who can write bash and other scripts to string together TubeTK's command-line tools | |||
We are working to provide modules, based on TubeTK, that allow TubeTK's methods to be called from within Slicer, Osirix, and ImageJ. | |||
If you have questions regarding or suggestions for improving TubeTK, please do not hesitate to contact the development team. | |||
== Features == | == Features == |
Revision as of 11:07, 14 April 2012
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OverviewTubeTK is being developed to host algorithms for applications involving images of tubes (blood vessel in medical images, roads in satellite images, etc.). It also offers methods for handling other geometries (points, surfaces, and densities) in images. By focusing on local geometry structure, the algorithms are able to accomplish segmentations, registrations, and other analyses that consider the physicial properties of objects and their variations, while not requiring limiting assumptions on the specific arrangement or general shape of the objects in the images. We are applying these techniques to push image understanding in new directions such as:
At this time TubeTK is targeted for
We are working to provide modules, based on TubeTK, that allow TubeTK's methods to be called from within Slicer, Osirix, and ImageJ. If you have questions regarding or suggestions for improving TubeTK, please do not hesitate to contact the development team. Features
Driving Applications
Technical Focus
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