Modules:RobustStatisticsSeg-Documentation-3.6 - Revision history

Ygao: /* References */

2012-07-16T11:16:16Z

References

2011-05-10T14:24:39Z

Use Cases, Examples

2011-05-10T14:21:44Z

Use Cases, Examples

2011-05-10T14:18:42Z

Use Cases, Examples

2011-05-10T14:16:16Z

Robust Statistics Segmentation

2011-03-01T16:01:05Z

Source code & documentation

2011-03-01T16:00:26Z

Source code & documentation

2010-10-28T00:34:27Z

References

2010-08-20T21:25:36Z

References

2010-06-12T21:20:02Z

Tutorials

← Older revision		Revision as of 11:16, 16 July 2012
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	===References===		===References===
−		+	Yi Gao, Ron Kikinis, Sylvain Bouix, Martha Shenton, Allen Tannenbaum, ''A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours'', Medical Image Analysis, 2012, http://dx.doi.org/10.1016/j.media.2012.06.002
−	Y. Gao, A. Tannenbaum~~, R. Kikinis~~, ''~~Simultaneous~~ Multi-~~Object~~ Segmentation using Local Robust Statistics ~~and Contour Interaction~~'', ~~MICCAI 2010~~, ~~Medical Computer Vision~~, ~~Workshop on [[File~~:~~Rss~~.~~pdf\|pdf]]~~

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 Image:RSS-aorta.png|Aorta, CT image(512*512*204). IO time: 10sec, module running time: 12sec (Intel 3.0GHz), <br>Approximate volume: 60 ml, <br>Intensity homogeneity: 1.0,  <br>Boundary smoothness: 0.0
 Image:RSSMandible.png|Head, CT image(512*512*460) from http://pubimage.hcuge.ch:8080/ MANIX data set. IO time: 16sec, module running time: 160sec (Intel 3.0GHz), <br>Approximate volume: 100 ml, <br>Intensity homogeneity: 0.5,  <br>Boundary smoothness: 0.0
-Image:rss_CT_liver.png|Liver, ROI cropped from CT image(512*512*204). Running time: 150sec (Intel 3.0GHz), <br>Volume limit: 2000 ml, <br>Intensity homogeneity: 0.9, <br>Boundary smoothness: 0.7
+Image:rss_CT_liver.png|Liver, ROI cropped from CT image(512*512*204). Running time: 150sec (Intel 3.0GHz), <br>Volume limit: 2000 ml, <br>Intensity homogeneity: 0.9, <br>Boundary smoothness: 0.7. Test case file [[File:CT_liver_segmentation_case.tgz]]
 </gallery>

@@ Line 41: / Line 41: @@
 Image:RSS-aorta.png|Aorta, CT image(512*512*204). IO time: 10sec, module running time: 12sec (Intel 3.0GHz), <br>Approximate volume: 60 ml, <br>Intensity homogeneity: 1.0,  <br>Boundary smoothness: 0.0
 Image:RSSMandible.png|Head, CT image(512*512*460) from http://pubimage.hcuge.ch:8080/ MANIX data set. IO time: 16sec, module running time: 160sec (Intel 3.0GHz), <br>Approximate volume: 100 ml, <br>Intensity homogeneity: 0.5,  <br>Boundary smoothness: 0.0
-Image:rss_CT_liver.png|Liver, ROI cropped from CT image(512*512*204). Running time: 12sec (Intel 3.0GHz), <br>Volume limit: 2000 ml, <br>Intensity homogeneity: 0.9, <br>Boundary smoothness: 0.7
+Image:rss_CT_liver.png|Liver, ROI cropped from CT image(512*512*204). Running time: 150sec (Intel 3.0GHz), <br>Volume limit: 2000 ml, <br>Intensity homogeneity: 0.9, <br>Boundary smoothness: 0.7
 </gallery>

@@ Line 41: / Line 41: @@
 Image:RSS-aorta.png|Aorta, CT image(512*512*204). IO time: 10sec, module running time: 12sec (Intel 3.0GHz), <br>Approximate volume: 60 ml, <br>Intensity homogeneity: 1.0,  <br>Boundary smoothness: 0.0
 Image:RSSMandible.png|Head, CT image(512*512*460) from http://pubimage.hcuge.ch:8080/ MANIX data set. IO time: 16sec, module running time: 160sec (Intel 3.0GHz), <br>Approximate volume: 100 ml, <br>Intensity homogeneity: 0.5,  <br>Boundary smoothness: 0.0
 </gallery>

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 {|
 |[[Image:RssPanel.png|thumb|280px|RSS module panel]]
 |[[Image:RssVentricle.png|thumb|400px|RSS result for Brain MR image]]
 |}

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 The source code of the algorithm is in 3D Slicer source directory tree. Under Slicer3/Applications/CLI/RobustStatisticsSegmenter/
-Moreover, the code above is a self-contained implementation for the 3D Sparse Filed Level Set method. The level set evolution is implemented in the base class. Specific flow, such as Chan-Vese, Geodesic Active Contour, or the Robust Statistics based flow, can be implemented in the derived class. Therefore, it can be reused for other types of flow with not much work.
+Moreover, the code above is a self-contained implementation for the 3D Sparse Filed Level Set method. The level set evolution is implemented in the base class. Specific flow, such as Chan-Vese, Geodesic Active Contour, or the Robust Statistics based flow, can be implemented as the derived class. Therefore, it can be reused for other types of flow with not much work.
 == More Information ==

@@ Line 135: / Line 135: @@
 ===Source code & documentation===
-Links to the module's source code:
+The source code of the algorithm is in 3D Slicer source directory tree. Under Slicer3/Applications/CLI/RobustStatisticsSegmenter/
-Source code:
+Moreover, the code above is a self-contained implementation for the 3D Sparse Filed Level Set method. The level set evolution is implemented in the base class. Specific flow, such as Chan-Vese, Geodesic Active Contour, or the Robust Statistics based flow, can be implemented in the derived class. Therefore, it can be reused for other types of flow with not much work.
 == More Information ==

← Older revision		Revision as of 00:34, 28 October 2010
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	===References===		===References===

−	Y. Gao, A. Tannenbaum, R. Kikinis, ''Simultaneous Multi-Object Segmentation using Local Robust Statistics and Contour Interaction'', MICCAI 2010, Medical Computer Vision, Workshop on	+	Y. Gao, A. Tannenbaum, R. Kikinis, ''Simultaneous Multi-Object Segmentation using Local Robust Statistics and Contour Interaction'', MICCAI 2010, Medical Computer Vision, Workshop on [[File:Rss.pdf\|pdf]]

← Older revision		Revision as of 21:25, 20 August 2010
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	===References===		===References===
		+
		+	Y. Gao, A. Tannenbaum, R. Kikinis, ''Simultaneous Multi-Object Segmentation using Local Robust Statistics and Contour Interaction'', MICCAI 2010, Medical Computer Vision, Workshop on

@@ Line 71: / Line 71: @@
-* Data Set http://www.spl.harvard.edu/publications/bitstream/download/4217 (case3/grayscale.nrrd)
+* Data Set http://www.spl.harvard.edu/publications/item/view/1180 Tumorbase.zip at page bottom, in the zip file, case3/grayscale.nrrd
 . Draw label map in Editing module
 In the editing module, select any drawing tool, for example the "Paint" tool circled in red. Draw some strokes in the target in one of the 2D views. In this example, we only freely draw the "S" shaped label circled in green.