Projects:LabelSpace - Revision history

Ygao at 01:00, 16 November 2013

2013-11-16T01:00:00Z

2012-01-12T16:12:14Z

2010-05-11T19:45:50Z

2009-08-27T20:22:13Z

2009-08-25T17:42:50Z

2009-07-17T17:52:53Z

2009-07-11T19:24:05Z

2009-06-27T06:40:17Z

added MICCAI reference

2008-04-24T17:13:26Z

2008-04-24T17:12:56Z

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-  Back to [[Algorithm:BU|Boston University Algorithms]]
+  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
 __NOTOC__
 = Label Space: A Coupled Multi-Shape Representation =

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-  Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
+  Back to [[Algorithm:BU|Boston University Algorithms]]
 __NOTOC__
 = Label Space: A Coupled Multi-Shape Representation =

@@ Line 40: / Line 40: @@
 = Publications =
-* [http://www.na-mic.org/publications/pages/display?search=LabelSpace&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]
+* [http://www.na-mic.org/publications/pages/display?search=LabelSpace&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database on Label Space: A Coupled Multi-Shape Representation]
 [[Category: Shape Analysis]]

@@ Line 1: / Line 1: @@
-Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
+ Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
 __NOTOC__
 = Label Space: A Coupled Multi-Shape Representation =

@@ Line 39: / Line 39: @@
 = Publications =
 * [http://www.na-mic.org/publications/pages/display?search=LabelSpace&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database]
 [[Category: Shape Analysis]]

← Older revision		Revision as of 19:24, 11 July 2009
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	J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.		J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

		+	* [http://www.na-mic.org/publications/pages/display?search=LabelSpace&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked\| NA-MIC Publications Database]

	[[Category: Shape Analysis]]		[[Category: Shape Analysis]]

← Older revision		Revision as of 06:40, 27 June 2009
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	= Publications =		= Publications =
		+
		+	J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Coupled Multi-Shape Representation." In MICCAI, 2008.

	J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.		J. Malcolm, Y. Rathi, and A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

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 techniques for such analysis tend to trade off performance between the two
 tasks, performing well for one task but developing problems when used for
-the other.
+the other.  We propose label space, a representation that is both flexible
 and well suited for both tasks.  Under this framework, object labels are
 mapped to vertices of a regular simplex, e.g. the unit interval for two
 labels, a triangle for three labels, a tetrahedron for four labels, etc.
 This forms a linear space with the property that all labels are equally
-spaced.
+spaced.  On examination, this representation has several desirable properties:
 algebraic operations may be done directly, label uncertainty is expressed as
 a weighted mixture of labels, interpolation is unbiased toward any label or
-the background, and registration may be performed directly.
+the background, and registration may be performed directly.  To demonstrate these properties, we describe variational registration
 directly in this space.  Many registration methods fix one of the maps and
 align the rest of the set to this fixed map.  To remove the bias induced by

@@ Line 12: / Line 12: @@
 = Description =
-The surfaces are represented as the zero level set of a signed distance function and shape learning is performed on the embeddings of these shapes. We carry out some experiments to see how well each of these methods can represent a shape, given the training set. We tested the performance of these methods on shapes of left caudate nucleus and left hippocampus. The training set of left caudate nucleus consisted of 26 data sets and the test set contained 3 volumes. Error between a particular shape representation and
+Two key aspects of coupled multi-object shape analysis are the choice of
-ground truth was calculated by computing the number of mislabeled voxels using each of the methods. Figure 1 gives the error
+representation and subsequent registration to align the sample set.  Current
-using each of the methods. Similar tests were done on a training set of 20 hippocampus data with 3 test volumes. Figure 2 gives the error table for each of the methods [1].
+techniques for such analysis tend to trade off performance between the two
 [[Image:Label_space.png|thumb|Figure 1: The first three label space configurations: a unit interval for two labels, a triangle for three labels, and a tetrahedron for four labels (left to right). ]]