Documentation/Nightly/Modules/ROBEXBrainExtraction - Revision history

Acsenrafilho at 13:07, 25 July 2017

2017-07-25T13:07:20Z

Acsenrafilho at 16:25, 21 July 2017

2017-07-21T16:25:43Z

Acsenrafilho at 16:53, 14 December 2016

2016-12-14T16:53:55Z

Acsenrafilho at 15:34, 26 November 2016

2016-11-26T15:34:49Z

Acsenrafilho at 13:01, 26 November 2016

2016-11-26T13:01:20Z

Acsenrafilho at 12:50, 26 November 2016

2016-11-26T12:50:55Z

Acsenrafilho: Created page with "{{documentation/versioncheck}} {{documentation/{{documentation/version}}/module-header}}

@@ Line 50: / Line 50: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Panels and their use}}
-[[Image:ROBEXBrainExtraction-GUI.png|thumb|482px|User Interface]]
+[[Image:ROBEXBrainExtraction-GUI.png|thumb|300px|User Interface]]
-*Input Volume:
+*'''Input Volume:'''
 **Insert an input scalar volume
-*Output Volume:
+*'''Output Volume:'''
 **Set the output volume
-*Brain Mask:
+*'''Brain Mask:'''
 **Set, if desired, the output binary mask resulted from the skull stripping process.
@@ Line 67: / Line 67: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|References}}
-* [http://dcm.ffclrp.usp.br/csim Computing in Signals and Images in Medicine Laboratory (CSIM)]
+* Iglesias JE, Liu CY, Thompson P, Tu Z: "Robust Brain Extraction Across Datasets and Comparison with Publicly Available Methods", IEEE Transactions on Medical Imaging, 30(9), 2011, 1617-1634.
 Repositories:
 * Source code: [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction GitHub repository]
 * Issue tracker:  [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction/issues open issues and enhancement requests]

@@ Line 24: / Line 24: @@
 {{documentation/{{documentation/version}}/module-description}}
 [[Image:ROBEXBrainExtraction-logo.png|left]]
-This module aims to extracted the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. <ref> Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634</ref>. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the [http://doi.org/10.1109/TMI.2011.2138152 original paper] and also the [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction module repository] in order to see the licenses applied in this project.
+This module aims to extract the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. <ref> Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634</ref>. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the [http://doi.org/10.1109/TMI.2011.2138152 original paper] and also the [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction module repository] in order to see the licenses applied in this project.
 In addition, for better brain volume extraction results it could be applied some preprocessing steps such as a noise attenuation and field inhomogeneity correction methods in advance. See the modules [[Documentation/{{documentation/version}}/Modules/AADImageFilter|AAD Image Filter]] and [[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection|N4 Bias Field Correction]].
@@ Line 55: / Line 55: @@
 *Output Volume:
 **Set the output volume
-'''NOTE''': The ROBEX brain extraction method is fully automatic and does not requires any input value.
+'''NOTE''': The ROBEX brain extraction method is fully automatic and does not require any input parameter.
 <!-- ---------------------------- -->

@@ Line 38: / Line 38: @@
 **Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation as well as the global brain atrophy.
-<gallery>
+<gallery widths="300px" heights="300px" perrow="3">
 Image:AllHeadMRI.png|T1w MRI
 Image:BrainMRI.png|Brain extracted T1 MRI

@@ Line 24: / Line 24: @@
 {{documentation/{{documentation/version}}/module-description}}
 [[Image:ROBEXBrainExtraction-logo.png|left]]
-This module aims to extracted the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. <ref> Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634</ref>. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the [http://doi.org/10.1109/TMI.2011.2138152 original paper]
+This module aims to extracted the brain in structural MRI images, namely T1 weighted MRI images. The method is based on the non-parametric algorithm proposed by Iglesias et. al. <ref> Iglesias, J.E. et al., 2011. "Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods". IEEE Transactions on Medical Imaging, 30(9), pp.1617–1634</ref>. The application with T2 and proton density MRI images were not intensively tested, but it is assumed that the algorithm is robust with those image contrasts as well. For more details about the brain extraction method, please check the [http://doi.org/10.1109/TMI.2011.2138152 original paper] and also the [https://github.com/CSIM-Toolkits/ROBEXBrainExtraction module repository] in order to see the licenses applied in this project.
 In addition, for better brain volume extraction results it could be applied some preprocessing steps such as a noise attenuation and field inhomogeneity correction methods in advance. See the modules [[Documentation/{{documentation/version}}/Modules/AADImageFilter|AAD Image Filter]] and [[Documentation/{{documentation/version}}/Modules/N4ITKBiasFieldCorrection|N4 Bias Field Correction]].

@@ Line 50: / Line 50: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Panels and their use}}
-[[Image:ROBEXBrainExtraction-GUI.png|thumb|380px|User Interface]]
+[[Image:ROBEXBrainExtraction-GUI.png|thumb|482px|User Interface]]
 *Input Volume:
 **Insert an input scalar volume
 *Output Volume:
 **Set the output volume
 <!-- ---------------------------- -->