Image registration using the Morphon algorithm: an ITK implementation
Université Catholique de Louvain (UCL, Belgium)
| Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/1527 |
Published in The Insight Journal - 2009 January - June.
Submitted by Jerome Plumat on 02-13-2009.
Medical image registration is becoming a more and more useful component of a large number of applications. The presented method aims to enrich the ITK library. This method, called Morphon registration algorithm, computes a dense deformation field accepting inputs from different intensity contrasts. This article presents its implementation within the Insight Toolkit.
In this paper, we provide a brief description of the algorithm, a presentation of the implementation, the justification of our modified classes and the results given by the algorithm. We demonstrate the algorithm in application of different images intesity constrasts and dimensions.
In this paper, we provide a brief description of the algorithm, a presentation of the implementation, the justification of our modified classes and the results given by the algorithm. We demonstrate the algorithm in application of different images intesity constrasts and dimensions.
Data
2 Datasets (346Kb)
Code
Automatic Testing Results
by Insight-Journal Dashboard
on Thu Mar 5 10:57:59 2009 for revision #4 
expertise: 5 sensitivity: 5 
This project passed all of its tests.Click here for more details.
Go here to access the main testing dashboard.
Reviews
Quick Comments
Comment by Huang Cheng
I changed the level numbers to 10 and it works without throwing any exception. I guess the number of slices in z-axis is too small, so after resampling 13 times, the number goes down to smaller than 4 and cause the exception.
Here is the parameter I used for testing:
10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5
I changed the level numbers to 10 and it works without throwing any exception. I guess the number of slices in z-axis is too small, so after resampling 13 times, the number goes down to smaller than 4 and cause the exception.
Here is the parameter I used for testing:
10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5 10 1.5
Comment by Huang Cheng
Could you please post the parameters you used for the 3D registration? I tried "10 1.5 10 1.5 10 4.5 10 4.5 10 4.5 10 2 10 2 10 2 10 2 10 2 10 2 10 3.5 10 2.5" you mentioned in the 2D registration case but failed. It keeps throw exception out.
Here is the exception information:
Description: itk::ERROR: RecursiveGaussianImageFilter(0000000002AA69D0): The number of pixels along direction 2 is less than 4. This filter requires a minimum of four pixels along the dimension to be processed.
Could you please post the parameters you used for the 3D registration? I tried "10 1.5 10 1.5 10 4.5 10 4.5 10 4.5 10 2 10 2 10 2 10 2 10 2 10 2 10 3.5 10 2.5" you mentioned in the 2D registration case but failed. It keeps throw exception out.
Here is the exception information:
Description: itk::ERROR: RecursiveGaussianImageFilter(0000000002AA69D0): The number of pixels along direction 2 is less than 4. This filter requires a minimum of four pixels along the dimension to be processed.
Resources
|
|
| Download Package | |
| Download Paper, View Paper | |
| Download Source code | |
| Github | |
Statistics more
|
|
| Global rating: |
|
| Review rating: | [review]
|
| Code rating: | 
|
| Paper Quality: |
|
| 3 comments | |
Information more
|
|
| Categories: | Deformable registration, PointSet, Registration |
| Keywords: | registration, Morphon, dense deformation field, |
| Toolkit: | ITK, CMake |
| Export citation: | |
Share
|
Associated Publications more
|
||
| Model-Image Registration of Parametric Shape Models: Fitting a Shell to the Cochlea | ||
View license
Loading license...
Send a message to the author
Hi,
I don't have the parameters (long time ago I did not have a look on this code!).
According to me the error is mostly due the fact that the size along z axis is not sufficient. The filters have sizes 9 (along all directions). Thus, you may have a sufficiently big volume to let the convolution take sense. The strict minimum is of course 9 but due to padding in the convolution you may ask for bigger sizes.
Also note that the 3D convolution is very slow in ITK!
Best