Serdar at 15:18, 11 January 2008

2008-01-11T15:18:54Z

Serdar: New page: {| |Return to [[2008_Winter_Project_Week ]] |valign="top"| [[Image:GroupwiseStackBiModal.PNG|thumb|350px|Figure 1: On the left is shown a stack of ima...

2008-01-11T15:16:45Z

New page: {| |thumb|320px|Return to [[2008_Winter_Project_Week ]] |valign="top"| [[Image:GroupwiseStackBiModal.PNG|thumb|350px|Figure 1: On the left is shown a stack of ima...

New page

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2008_Winter_Project_Week]] ]]
|valign="top"| [[Image:GroupwiseStackBiModal.PNG|thumb|350px|Figure 1: On the left is shown a stack of images
and a sample pixel stack around a cortical region. On the left is shown the Gaussian(blue) fittet to
a real sample from the dataset we used along with the non-parametric density estimate(red).
Note that the distribution is bi-modal because of white matter-gray matter transaction.]]
|}

__NOTOC__
===Key Investigators===
* MIT: Serdar Balci, Polina Golland
* Kitware: Luis Ibanez

<div style="margin: 20px;">

<div style="width: 27%; float: left; padding-right: 3%;">

<h1>Objective</h1>

The goal of this project is to develop an efficient B-Splines based registration code.
We explore the space of different optimization schemes e.g. caching, sparsity and memory-speed trade-off's.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h1>Approach, Plan </h1>

We run benchmark experiments in the special case of itkMattesMutualInformation implementation.
The current implementation in ITK uses caching of B-Spline coefficient in order to speed-up
the registration. Also the sparsity of B-Spline coefficient updates are taken into account, yielding
and efficient implementation.

The drawback of caching is the use of high amounts of memory.
We compare the performance of caching vs. not-caching(computing the coefficients on the fly)
by deactivating caching code in itkMattesMutualInformation .

</div>

<div style="width: 40%; float: left;">

<h1>Progress</h1>

====Jan 2008 Project Week====

We run an experiment on a machine
with 8GB of memory, 4 core CPU
running gcc compiler on a linux system.

We used a B-Spline with mesh size 32x32x16
with an image of size 256x256x124 and used
Mattes mutual information as the registration algorithm.

Run-times:
* 21 seconds with caching (2.4 GB of memory)
* 52 seconds w/o caching (800MB of memory)

Overall in this experiment, caching improved the performance by 2.5 while using 3 times more memory.
We are in the process of exploring the effect of number of samples, image size and compiler on
registration time.

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<br style="clear: both;" />

</div>

@@ Line 1: / Line 1: @@
 {|
 |[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2008_Winter_Project_Week]] ]]
-|valign="top"|   [[Image:GroupwiseStackBiModal.PNG|thumb|350px|Figure 1: On the left is shown a stack of images
+|valign="top"|
-and a sample pixel stack around a cortical region. On the left is shown the Gaussian(blue) fittet to
+[[Image:GroupwiseBspline.png|thumb|350px|Figure 2: An example deformation field. The local neighborhood affecting the deformation is overlayed on the image.]]
 |}

2008 Winter Project Week BSplineReg - Revision history

Serdar at 15:18, 11 January 2008

Serdar: New page: {| |Return to [[2008_Winter_Project_Week ]] |valign="top"| [[Image:GroupwiseStackBiModal.PNG|thumb|350px|Figure 1: On the left is shown a stack of ima...