Documentation/4.1/Modules/BRAINSDemonWarp - Revision history

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2013-06-14T07:25:27Z

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Dmwelch: dmwelch: moved algorithm description out of use cases

2012-04-18T21:50:27Z

dmwelch: moved algorithm description out of use cases

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2012-04-18T21:03:37Z

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2012-04-17T22:07:10Z

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2012-04-17T16:28:02Z

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JChris.FillionR: 4.0 -> 4.1

2012-04-12T04:41:02Z

4.0 -> 4.1

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{{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the [http://www.na-mic.org/ NA-MIC website].<br>
Author: Hans Johnson, UIOWA<br>
Contact: Hans Johnson, <email>(hans-johnson@uiowa.edu</email><br>
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|Image:UIOWA-logo.png|University of Iowa
|Image:Logo-splnew.jpg|Surgical Planning Laboratory
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{{documentation/{{documentation/version}}/module-section|Module Description}}
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{{documentation/{{documentation/version}}/module-section|Use Cases}}

The inputs to the BrainDemonsWarp program are the target image, the template image and the optional parameters. These parameters define the arguments for histogram matching and multi resolution registration. The outputs are the deformation field, output image, checkerboard image of the output and the fixed image and the x,y,z displacement vectors. If we specify debug option we can get the outputs at different stages. The filter is templated over the input image type, real image type and the output image types. We implement the algorithm by parsing the input, preprocessing them and registering the processed images.

* Use Case 1: Parsing - The images are initialized by the ValidationInputParser. This function reads in the arguments from the parameter file. It sets the histogram bins, match points, number of levels in the multi resolution registration, shrink factors and number of iterations at each levels. If the orientations of the images are different it sets the orientation of the moving image to that of the fixed image.
* Use Case 2: PreProcessing - In the next step the DemonsPreProcessor preprocesses the images by resampling the template image to target image space. The intensity mismatch problem is solved by histogram matching the images. Histogram matching is done only if the command line option -e is set. ItkHistogramMatchingImageFilter is used to perform this function. Another important step in preprocessing is skull stripping. Skull stripping is done only if the command line option -maskProcessingMode is set to BOBF. We have written an itk filter, named itkBOBFFilter for this purpose. This filter takes in an input image and a whole brain mask and outputs a Brain Only Background Filled(BOBF) image. The non-brain parts in the image are filled with the user specified background value. All computations are performed in the precision of ﬂoat data.
* Use Case 3: Registration - The resulting moving Image and the fixed image are given as inputs to the demons registrator.It uses the MultiResolutionPDEDeformableRegistration filter with NN extrapolation as interpolator and implements the demons deformable algorithm by computing the deformation field which will map a moving image onto a fixed image. It is assumed that the vector elements behave like ﬂoating point scalars. Each vector in the deformation field represent the distance between a geometric point in the input space and a point in the output space. The output image is generated by warping the input image with the deformation field using the ItkWarpImageFilter. WarpImageFilter warps an existing image with respect to a given deformation field. Typically the mapped position does not correspond to an integer pixel position in the input image. Interpolation via an image function is used to compute values at non-integer positions. We have used the LinearInterpolateImageFunction for our application. To write the output image we cast the image to the user specified output pixel type.


{{documentation/{{documentation/version}}/module-section|Tutorials}}
Links to tutorials that use this module


{{documentation/{{documentation/version}}/module-section|Panels and their use}}
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
* Point to other modules that have similar functionality


{{documentation/{{documentation/version}}/module-section|References}}
Publications related to this module go here. Links to pdfs would be useful.
For extensions: link to the source code repository and additional documentation


{{documentation/{{documentation/version}}/module-section|Information for Developers}}
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+# PreProcessing - In the next step the DemonsPreProcessor preprocesses the images by resampling the template image to target image space. The intensity mismatch problem is solved by histogram matching the images. Histogram matching is done only if the command line option -e is set. ItkHistogramMatchingImageFilter is used to perform this function. Another important step in preprocessing is skull stripping. Skull stripping is done only if the command line option -maskProcessingMode is set to BOBF. We have written an itk filter, named itkBOBFFilter for this purpose. This filter takes in an input image and a whole brain mask and outputs a Brain Only Background Filled(BOBF) image. The non-brain parts in the image are filled with the user specified background value. All computations are performed in the precision of ﬂoat data.
-## PreProcessing - In the next step the DemonsPreProcessor preprocesses the images by resampling the template image to target image space. The intensity mismatch problem is solved by histogram matching the images. Histogram matching is done only if the command line option -e is set. ItkHistogramMatchingImageFilter is used to perform this function. Another important step in preprocessing is skull stripping. Skull stripping is done only if the command line option -maskProcessingMode is set to BOBF. We have written an itk filter, named itkBOBFFilter for this purpose. This filter takes in an input image and a whole brain mask and outputs a Brain Only Background Filled(BOBF) image. The non-brain parts in the image are filled with the user specified background value. All computations are performed in the precision of ﬂoat data.
+# Registration - The resulting moving Image and the fixed image are given as inputs to the demons registrator.It uses the MultiResolutionPDEDeformableRegistration filter with NN extrapolation as interpolator and implements the demons deformable algorithm by computing the deformation field which will map a moving image onto a fixed image. It is assumed that the vector elements behave like ﬂoating point scalars. Each vector in the deformation field represent the distance between a geometric point in the input space and a point in the output space. The output image is generated by warping the input image with the deformation field using the ItkWarpImageFilter. WarpImageFilter warps an existing image with respect to a given deformation field. Typically the mapped position does not correspond to an integer pixel position in the input image. Interpolation via an image function is used to compute values at non-integer positions. We have used the LinearInterpolateImageFunction for our application. To write the output image we cast the image to the user specified output pixel type.
 The inputs to the BrainDemonsWarp program are
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 * the x,y,z displacement vectors.
 If we specify the --debug option we can get the outputs at different stages. The filter is templated over the input image type, real image type and the output image types. We implement the algorithm by parsing the inputs, preprocessing them and registering the processed images.
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-# """Registration"""
+# '''''''Registration'''''''
-# Parsing - The images are initialized by the ValidationInputParser. This function reads in the arguments from a parameter file.  It sets the histogram bins, match points, number of levels in the multi resolution registration, shrink factors and number of iterations at each levels. If the orientation of the images are different, it sets the moving image orientation to that of the fixed image.
+## Parsing - The images are initialized by the ValidationInputParser. This function reads in the arguments from a parameter file.  It sets the histogram bins, match points, number of levels in the multi resolution registration, shrink factors and number of iterations at each levels. If the orientation of the images are different, it sets the moving image orientation to that of the fixed image.
-# PreProcessing - In the next step the DemonsPreProcessor preprocesses the images by resampling the template image to target image space. The intensity mismatch problem is solved by histogram matching the images. Histogram matching is done only if the command line option -e is set. ItkHistogramMatchingImageFilter is used to perform this function. Another important step in preprocessing is skull stripping. Skull stripping is done only if the command line option -maskProcessingMode is set to BOBF. We have written an itk filter, named itkBOBFFilter for this purpose. This filter takes in an input image and a whole brain mask and outputs a Brain Only Background Filled(BOBF) image. The non-brain parts in the image are filled with the user specified background value. All computations are performed in the precision of ﬂoat data.
+## PreProcessing - In the next step the DemonsPreProcessor preprocesses the images by resampling the template image to target image space. The intensity mismatch problem is solved by histogram matching the images. Histogram matching is done only if the command line option -e is set. ItkHistogramMatchingImageFilter is used to perform this function. Another important step in preprocessing is skull stripping. Skull stripping is done only if the command line option -maskProcessingMode is set to BOBF. We have written an itk filter, named itkBOBFFilter for this purpose. This filter takes in an input image and a whole brain mask and outputs a Brain Only Background Filled(BOBF) image. The non-brain parts in the image are filled with the user specified background value. All computations are performed in the precision of ﬂoat data.
-# Registration - The resulting moving Image and the fixed image are given as inputs to the demons registrator.It uses the MultiResolutionPDEDeformableRegistration filter with NN extrapolation as interpolator and implements the demons deformable algorithm by computing the deformation field which will map a moving image onto a fixed image. It is assumed that the vector elements behave like ﬂoating point scalars. Each vector in the deformation field represent the distance between a geometric point in the input space and a point in the output space. The output image is generated by warping the input image with the deformation field using the ItkWarpImageFilter. WarpImageFilter warps an existing image with respect to a given deformation field. Typically the mapped position does not correspond to an integer pixel position in the input image. Interpolation via an image function is used to compute values at non-integer positions. We have used the LinearInterpolateImageFunction for our application. To write the output image we cast the image to the user specified output pixel type.
+## Registration - The resulting moving Image and the fixed image are given as inputs to the demons registrator.It uses the MultiResolutionPDEDeformableRegistration filter with NN extrapolation as interpolator and implements the demons deformable algorithm by computing the deformation field which will map a moving image onto a fixed image. It is assumed that the vector elements behave like ﬂoating point scalars. Each vector in the deformation field represent the distance between a geometric point in the input space and a point in the output space. The output image is generated by warping the input image with the deformation field using the ItkWarpImageFilter. WarpImageFilter warps an existing image with respect to a given deformation field. Typically the mapped position does not correspond to an integer pixel position in the input image. Interpolation via an image function is used to compute values at non-integer positions. We have used the LinearInterpolateImageFunction for our application. To write the output image we cast the image to the user specified output pixel type.
 The inputs to the BrainDemonsWarp program are
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 {{documentation/{{documentation/version}}/module-section|References}}
-Publications related to this module go here. Links to pdfs would be useful.
+* [http://hdl.handle.net/1926/1517 BRAINSDemonWarp: An Applicaton to Perform Demons Registration], Johnson H., Zhao Y., Insight Journal, 2009.
-For extensions: link to the source code repository and additional documentation
+* Source repository on [https://github.com/BRAINSia/BRAINSStandAlone/tree/master/BRAINSDemonWarp github]
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