Projects:MGH-HeadAndNeck-PtSetReg - Revision history

Ygao at 01:03, 16 November 2013

2013-11-16T01:03:34Z

Ivan.kolesov at 03:34, 19 November 2012

2012-11-19T03:34:46Z

Ivan.kolesov at 03:26, 19 November 2012

2012-11-19T03:26:45Z

Ivan.kolesov at 03:25, 19 November 2012

2012-11-19T03:25:57Z

Ivan.kolesov at 03:21, 19 November 2012

2012-11-19T03:21:17Z

Ivan.kolesov at 03:17, 19 November 2012

2012-11-19T03:17:51Z

Ivan.kolesov at 03:16, 19 November 2012

2012-11-19T03:16:09Z

Ivan.kolesov at 03:15, 19 November 2012

2012-11-19T03:15:38Z

Ivan.kolesov at 03:14, 19 November 2012

2012-11-19T03:14:48Z

Ivan.kolesov at 03:02, 19 November 2012

2012-11-19T03:02:21Z

@@ Line 1: / Line 1: @@
-  Back to [[Algorithm:BU|Boston University Algorithms]]
+  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
 __NOTOC__
 = Semi-Automatic Image Registration =

@@ Line 19: / Line 19: @@
 Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Again, an injective deformation field is computed.
-* [[Image:FleshMis.png | FleshMis| 600px]]  [[Image:FleshAlignedView1.png | FleshAlignedView1| 400px]]
+* [[Image:FleshMis.png | FleshMis| 400px]]  [[Image:FleshAlignedView1.png | FleshAlignedView1| 400px]]
 Point clouds representing flesh tissue of the patients (before and after registration). This step is constrained.

@@ Line 22: / Line 22: @@
 Point clouds representing flesh tissue of the patients (before and after registration). This step is constrained.
-The results of applying the two deformation computed by the proposed process are shown below.
+The result of applying the two deformations computed by the proposed process are shown below.
 * [[Image:PostRegFleshSkeleton.png | PostRegFleshSkeleton| 400px]]

@@ Line 3: / Line 3: @@
 = Semi-Automatic Image Registration =
-We recognize that the difference between a failure of an automatic image registration approach and a success of a semi-automatic method can be a small amount of user input. The goal of this work is to register two CT volumes of different patients that are related by a large deformation. The user sets two thresholds for each image: one for the bone mask and another for flesh tissue. This operation is not time consuming but simplifies the registration task dramatically for the automatic algorithm.
+We recognize that the difference between a failure of an automatic image registration approach and a success of a semi-automatic method can be a small amount of user input. The goal of this work is to register two CT volumes of different patients that are related by a large misalignment. The user sets two thresholds for each image: one for the bone mask and another for the flesh tissue. This operation is not time consuming but simplifies the registration task dramatically for the automatic algorithm.
 = Description =
@@ Line 17: / Line 17: @@
-Another set of point clouds is generated by sampling from label maps of flesh created for the images from the previous step. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Again, an injective deformation field is computed.
+Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Again, an injective deformation field is computed.
-* [[Image:FleshMis.png | FleshMis| 400px]]  [[Image:FleshAlignedView1.png | FleshAlignedView1| 400px]]
+* [[Image:FleshMis.png | FleshMis| 600px]]  [[Image:FleshAlignedView1.png | FleshAlignedView1| 400px]]
 Point clouds representing flesh tissue of the patients (before and after registration). This step is constrained.

@@ Line 17: / Line 17: @@
-Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Again, an injective deformation field is computed.
+Another set of point clouds is generated by sampling from label maps of flesh created for the images from the previous step. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Again, an injective deformation field is computed.
 * [[Image:FleshMis.png | FleshMis| 400px]]  [[Image:FleshAlignedView1.png | FleshAlignedView1| 400px]]
 Point clouds representing flesh tissue of the patients (before and after registration). This step is constrained.
-The result of
+The results of applying the two deformation computed by the proposed process are shown below.

← Older revision		Revision as of 03:17, 19 November 2012
Line 17:		Line 17:


−	Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. ~~Another~~ injective deformation field is computed.	+	Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Again, an injective deformation field is computed.

		+	* [[Image:FleshMis.png \| FleshMis\| 400px]] [[Image:FleshAlignedView1.png \| FleshAlignedView1\| 400px]]
		+	Point clouds representing flesh tissue of the patients (before and after registration). This step is constrained.

	The result of		The result of

@@ Line 8: / Line 8: @@
 In this example, large misalignment is present between the two patients.
-* [[Image:PreRegFleshSkeleton.png | PreRegFleshSkeleton| 200px]]
+* [[Image:PreRegFleshSkeleton.png | PreRegFleshSkeleton| 400px]]
 Original Misalignment of the volumes.
 Point clouds are generated from label maps of bone. The computed registration field, which is guaranteed to be injective, is applied to the original CT volumes.
-* [[Image:SkeletonMisalignedView1.png | SkeletonMisalignedView1| 200px]]  [[Image:SkeletonAlignedView1.png | SkeletonMisalignedView1| 200px]]
+* [[Image:SkeletonMisalignedView1.png | SkeletonMisalignedView1| 400px]]  [[Image:SkeletonAlignedView1.png | SkeletonMisalignedView1| 400px]]
 Point clouds representing bone tissue of the patients (before and after registration).

@@ Line 8: / Line 8: @@
 In this example, large misalignment is present between the two patients.
 Point clouds are generated from label maps of bone. The computed registration field, which is guaranteed to be injective, is applied to the original CT volumes.
-* [[Image:KSliceFlowChart.png | PreRegFleshSkeleton| 800px]]
+* [[Image:SkeletonMisalignedView1.png | SkeletonMisalignedView1| 800px]]
-Flowchart for the interactive segmentation approach. Notice the user's pivotal role in the process.
+* [[Image:SkeletonAlignedView1.png | SkeletonMisalignedView1| 800px]]

@@ Line 6: / Line 6: @@
 = Description =
-In this work, interactive segmentation is integrated with an active contour model and segmentation is posed as a human-supervisory control problem. User input is tightly coupled with an automatic segmentation algorithm leveraging the user's high-level anatomical knowledge and the automated method's speed. Real-time visualization enables the user to quickly identify and correct the result in a sub-domain where the variational model's statistical assumptions do not agree with his expert knowledge. Methods developed in this work are applied to magnetic resonance imaging (MRI) volumes as part of a population study of human skeletal development. Segmentation time is reduced by approximately five times over similarly accurate manual segmentation of large bone structures.
+In this example, large misalignment is present between the two patients.
-* [[Image:KSliceFlowChart.png | Rel Pred| 800px]]
+Point clouds are generated from label maps of bone. The computed registration field, which is guaranteed to be injective, is applied to the original CT volumes.
 Flowchart for the interactive segmentation approach. Notice the user's pivotal role in the process.
-* [[Image:KSliceInptTimeChart.png | Eye Seg| 800px]]
-Time-line of user input into the system. Note that user input is sparse, has local effect only, and decreases in frequency and magnitude over time.
-* [[Image:KVoutSegTightMod.png | Eye Seg| 300px]]
+Another set of point clouds is generated by sampling from label maps of flesh. To avoid undoing the previous registration, regions belonging to the registered bone tissue from above are constrained not to move. Another injective deformation field is computed.
-Result of the segmentation.
 == Current State of Work ==