Difference between revisions of "2016 Winter Project Week/Projects/ShapeAnalysis"
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Statistical shape analysis develops methods for the geometric study of objects. | Statistical shape analysis develops methods for the geometric study of objects. | ||
A common way to represent shapes for a group of images is the geometric transformation between each individual and the mean image. | A common way to represent shapes for a group of images is the geometric transformation between each individual and the mean image. | ||
| − | Analyzing diffeomorphic shape changes can be linked to disease processes and changes in cognitive and behavioral measures.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. | + | Analyzing diffeomorphic shape changes can be linked to disease processes and changes in cognitive and behavioral measures. 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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| − | * | + | * Derived mathematical framework |
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Latest revision as of 16:03, 8 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. A common way to represent shapes for a group of images is the geometric transformation between each individual and the mean image. Analyzing diffeomorphic shape changes can be linked to disease processes and changes in cognitive and behavioral measures. 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.
