Difference between revisions of "2016 Winter Project Week/Projects/ShapeAnalysis"
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==Project Description== | ==Project Description== | ||
| − | Statistical shape analysis develops methods for the geometric study of objects. | + | Statistical shape analysis develops methods for the geometric study of objects. Analyzing |
| − | images is the geometric transformation between each individual and the mean image. One challenge of shape variability quantification is | + | diffeomorphic shape changes can be linked to disease processes and changes in cognitive and behavioral measures. |
| − | 'the curse of dimensionality', for instance, the transformation grid 128x128x128 as a shape descriptor for a 3D brain image. This makes the inference procedure computationally complicate and time-consuming. An efficient method needs to be developed to handle this complex dataset. | + | A common way to represent shapes for a group of images is the geometric transformation between each individual and the mean image. One challenge of shape variability quantification is 'the curse of dimensionality', for instance, the transformation grid 128x128x128 as a shape descriptor for a 3D brain image. This makes the inference procedure computationally complicate and time-consuming. An efficient method needs to be developed to handle this complex dataset. |
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Revision as of 18:20, 4 January 2016
Home < 2016 Winter Project Week < Projects < ShapeAnalysis
Key Investigators
- Miaomiao Zhang (MIT)
- Polina Golland (MIT)
Project Description
Statistical shape analysis develops methods for the geometric study of objects. Analyzing diffeomorphic shape changes can be linked to disease processes and changes in cognitive and behavioral measures. A common way to represent shapes for a group of images is the geometric transformation between each individual and the mean image. One challenge of shape variability quantification is 'the curse of dimensionality', for instance, the transformation grid 128x128x128 as a shape descriptor for a 3D brain image. This makes the inference procedure computationally complicate and time-consuming. An efficient method needs to be developed to handle this complex dataset.
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Background and References
Bayesian Principal Geodesic Analysis for Estimating Intrinsic Diffeomorphic Image Variability, Miaomiao Zhang and P. T. Fletcher, MICCAI, 2014.
