Difference between revisions of "2016 Winter Project Week/Projects/ShapeAnalysis"
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==Key Investigators== | ==Key Investigators== | ||
*Miaomiao Zhang (MIT) | *Miaomiao Zhang (MIT) | ||
| − | Polina Golland (MIT) | + | *Polina Golland (MIT) |
<!-- Add a bulleted list of investigators and their institutions here --> | <!-- Add a bulleted list of investigators and their institutions here --> | ||
==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. |
| − | images is the geometric transformation between each individual and the mean image. One challenge of shape variability quantification is | + | A common way to represent shapes for a group of images is the geometric transformation between each individual and the mean image. |
| − | '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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<!-- Approach and Plan bullet points --> | <!-- Approach and Plan bullet points --> | ||
| − | * | + | * Talk to Miaomiao |
| | | | ||
<!-- Progress and Next steps bullet points (fill out at the end of project week --> | <!-- Progress and Next steps bullet points (fill out at the end of project week --> | ||
| − | * | + | * Derived mathematical framework |
| + | * Try to test the method and solve real problems | ||
|} | |} | ||
==Background and References== | ==Background and References== | ||
| + | Bayesian Principal Geodesic Analysis for Estimating Intrinsic Diffeomorphic Image Variability, Miaomiao Zhang and P. T. Fletcher, MICCAI, 2014. | ||
<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --> | <!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data --> | ||
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.
| Objective | Approach and Plan | Progress and Next Steps |
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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.
