2016 Winter Project Week/Projects/ShapeAnalysis - Revision history

Miaomiao: /* Project Description */

2016-01-08T16:03:28Z

Project Description

2016-01-04T19:09:25Z

Project Description

2016-01-04T18:48:42Z

Project Description

2016-01-04T18:22:17Z

Project Description

2016-01-04T18:20:53Z

Project Description

2016-01-04T17:58:39Z

Background and References

2016-01-04T17:53:47Z

Key Investigators

2016-01-04T17:53:31Z

Key Investigators

2016-01-04T17:52:34Z

Key Investigators

2016-01-04T17:47:29Z

Project Description

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+* Derived mathematical framework
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+*  Talk to Miaomiao
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 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.
+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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 ==Project Description==
-Statistical shape analysis develops methods for the geometric study of objects. Analyzing
+Statistical shape analysis develops methods for the geometric study of objects.
-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.
-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.
+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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@@ Line 11: / Line 11: @@
 ==Project Description==
-Statistical shape analysis develops methods for the geometric study of objects. The means to represent shapes for a group of
+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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← Older revision		Revision as of 17:58, 4 January 2016
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	==Background and References==		==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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 ==Key Investigators==
 *Miaomiao Zhang (MIT)
-Polina Golland (MIT)
+*Polina Golland (MIT)
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← Older revision		Revision as of 17:52, 4 January 2016
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	==Key Investigators==		==Key Investigators==
		+	Miaomiao Zhang, CSAIL, MIT
		+	Polina Golland, CSAIL, MIT
	<!-- Add a bulleted list of investigators and their institutions here -->		<!-- Add a bulleted list of investigators and their institutions here -->