View source for 2016 Winter Project Week/Projects/ShapeAnalysis

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Image:PW-MIT2016.png|link=2016_Winter_Project_Week#Projects|[[2016_Winter_Project_Week#Projects|Projects List]]
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==Key Investigators==
*Miaomiao Zhang (MIT)
*Polina Golland (MIT)
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==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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! style="text-align: left; width:27%" |   Objective
! style="text-align: left; width:27%" |   Approach and Plan
! style="text-align: left; width:27%" |   Progress and Next Steps
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* Develop a low-dimensional statistical shape analysis method on the manifold of diffeomorphic transformations.
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* 
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*
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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.
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