Projects:pfPtSetImgReg - Revision history

Ygao at 01:00, 16 November 2013

2013-11-16T01:00:50Z

Ygao at 15:47, 12 January 2012

2012-01-12T15:47:56Z

Melonakos at 18:36, 20 October 2009

2009-10-20T18:36:14Z

Melonakos at 18:36, 20 October 2009

2009-10-20T18:36:00Z

Melonakos at 17:31, 20 July 2009

2009-07-20T17:31:24Z

Ygao: /* Prostate registration */

2009-04-12T20:16:36Z

Prostate registration

Ygao: Created page with 'Back to Georgia Tech Algorithms NOTOC = Particle Filter Registration of Medical Imagery = We transform the general problem of image registration to a sp...'

2009-04-12T20:13:39Z

Created page with 'Back to Georgia Tech Algorithms __NOTOC__ = Particle Filter Registration of Medical Imagery = We transform the general problem of image registration to a sp...'

New page

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Particle Filter Registration of Medical Imagery =

We transform the general problem of image registration to a sparse task of aligning point clouds. This is accomplished by considering the image as a probability density function, from which a point cloud is formed by randomly drawing samples. This allows us to apply a particle filtering technique for the registration. While point set registration and image registration are generally studied separately, this approach attempts to bridge the two. Thus, our contribution is threefold. Firstly, our method can handle affine transformations. Secondly, registration of partial images is more natural. Lastly, the point cloud representation is much sparser than the usual image representation as a discrete function. This allows us to drastically reduce the computational cost associated with 2D and 3D registration task.

== Prostate registration ==
Experimental results demonstrate the fast and robust convergence of the proposed algorithm. See Figures below:

One the right side:
Blue: prostate in prone position;
Yellow: prostate in supine position.

One the left:
Blue: The same prostate in prone position;
Pink: Result of registering the Yellow (supine) towards the Blue (prone).

[[Image:ProstateRegSupineToProneInParaview.png|500px|]]

== DWI registration ==
When applying the method to register two point sets generated from DWI images, the local minima of the registration energy is successfully avoided and the figure below shows:

[[Image:BrainBefore.png|500px|]]

Shows the original DWI point set in blue and the affine transformed one in red. The objective is to register the red to the blue.

[[Image:BrainAfter.png|500px|]]

The result of using the particle filter image registration.

= Key Investigators =

* Georgia Tech Algorithms: Yi Gao, Allen Tannenbaum

= Publications =

[[Category: Segmentation]] [[Category: Prostate]]

@@ Line 1: / Line 1: @@
-  Back to [[Algorithm:BU|Boston University Algorithms]]
+  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
 __NOTOC__
 = Particle Filter Registration of Medical Imagery =

@@ Line 38: / Line 38: @@
 ''In Press''
- Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission)
+Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission)
 [[Category: Segmentation]] [[Category: Prostate]]

@@ Line 5: / Line 5: @@
 We transform the general problem of image registration to a sparse task of aligning point clouds. This is accomplished by considering the image as a probability density function, from which a point cloud is formed by randomly drawing samples. This allows us to apply a particle filtering technique for the registration. While point set registration and image registration are  generally studied separately, this approach attempts to bridge the two. Thus, our contribution is threefold. Firstly, our method can handle affine transformations. Secondly, registration of partial images is more natural. Lastly, the point cloud representation is much sparser than the usual image representation as a discrete function. This allows us to drastically reduce the computational cost associated with 2D and 3D registration task.
-== Prostate registration ==
+= Description =
 Experimental results demonstrate the fast and robust convergence of the proposed algorithm. See Figures below:
@@ Line 35: / Line 35: @@
 = Publications =
 [[Category: Segmentation]] [[Category: Prostate]]

@@ Line 9: / Line 9: @@
 One the right side:
-     Blue: prostate in prone position;
+     Blue: prostate in '''prone''' position;
-     Yellow: prostate in supine position.
+     Yellow: prostate in '''supine''' position.
 One the left:

Projects:pfPtSetImgReg - Revision history

Ygao at 01:00, 16 November 2013

Ygao at 15:47, 12 January 2012

Melonakos at 18:36, 20 October 2009

Melonakos at 18:36, 20 October 2009

Melonakos at 17:31, 20 July 2009

Ygao: /* Prostate registration */

Ygao: Created page with 'Back to Georgia Tech Algorithms __NOTOC__ = Particle Filter Registration of Medical Imagery = We transform the general problem of image registration to a sp...'

Ygao: Created page with 'Back to Georgia Tech Algorithms NOTOC = Particle Filter Registration of Medical Imagery = We transform the general problem of image registration to a sp...'