Difference between revisions of "Projects:Utah2ShapeRegression"
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Segmentation of multi-time point image data of individual subjects results in serial shape data of structures of interest. Clinical research is interested in the spatiotemporal analysis of shape changes, which potentially leads to improved understanding of the rate of change, locality and growth trajectory of structures of interest. | Segmentation of multi-time point image data of individual subjects results in serial shape data of structures of interest. Clinical research is interested in the spatiotemporal analysis of shape changes, which potentially leads to improved understanding of the rate of change, locality and growth trajectory of structures of interest. | ||
Revision as of 03:24, 12 April 2011
Home < Projects:Utah2ShapeRegressionSmooth Growth Trajectories from Time Series Shape Data
Back to NA-MIC Collaborations, Utah2 Algorithms, MIT Algorithms, UNC Algorithms
Segmentation of multi-time point image data of individual subjects results in serial shape data of structures of interest. Clinical research is interested in the spatiotemporal analysis of shape changes, which potentially leads to improved understanding of the rate of change, locality and growth trajectory of structures of interest.
