update NFT

plugins/NFT/Chapter_01_Getting_Started_with_NFT.md → plugins/NFT/Chapter-01-Getting-Started-with-NFT.md

@@ -1,9 +1,11 @@
 ---
 layout: default
-title: NFT
-long_title: NFT
 parent: NFT
 grand_parent: Plugins
+render_with_liquid: false
+
+title: Chapter-01-Getting-Started-with-NFT
+long_title: Chapter-01-Getting-Started-with-NFT
 ---
 Introduction
 ------------

plugins/NFT/Chapter_02_Head_Modeling_from_MR_Images.md → plugins/NFT/Chapter-02-Head-Modeling-from-MR-Images.md

@@ -1,21 +1,19 @@
 ---
 layout: default
-title: NFT
-long_title: NFT
 parent: NFT
 grand_parent: Plugins
+render_with_liquid: false
+
+title: Chapter-02-Head-Modeling-from-MR-Images
+long_title: Chapter-02-Head-Modeling-from-MR-Images
 ---
 The steps of head modeling are segmentation, mesh generation, and
 co-registration of electrode locations with scalp surface. User may also
 generate a source space to be used in the solution of the inverse
 problem. Figure 2 shows the steps of head modeling using MR images.
 
-<center>
-
 ![Figure 2: steps of head modeling using MR images](NFM_Toolboox_UsersManual_html_2aaa1b22.gif)
 
-</center>
-
 Each step in realistic head modeling is implemented as a separate GUI
 module reachable from the main menu. These modules are described in the
 following sub-sections.
@@ -51,13 +49,9 @@ panel allows the user to select which image to display on the image
 panels. The available choices are the MR volume, the filtered volume or
 various stages of segmentation.
 
-<center>
-
 ![](NFT_from_MRI_segmentation.png) .....
 ![Figure 3: Interface for segmentation](NFT_segmentation.png)
 
-</center>
-
 The panel on the right of the segmentation GUI shows the segmentation
 steps that will be performed on the volume in order:
 
@@ -111,15 +105,11 @@ watershed segmentation algorithm, and the default values work for most
 images. The result of Brain segmentation can be seen by selecting “Brain
 Mask”.
 
-<center>
-
 ![Figure 4: Interface of segmentation during setting lowest point
 for cerebellum.](NFT_cerebellarlowpoint.png "wikilink") ......
 ![Figure 5: Interface of segmentation during a seed point
 selection on WM.](NFT_WMpointselection.png "wikilink")
 
-</center>
-
 ### Outer skull segmentation
 
 For outer skull segmentation, seed points for the eye lobes are selected
@@ -130,17 +120,11 @@ click on both eye lobes. Figure 7 shows the matlab figure that pops-up
 to click on eye lobes. Once the eyes are selected the outer skull is
 segmented and can be seen by “Outer skull mask”.
 
-<center>
-
 ![Figure 6: Interface of segmentation to select an axial slice where the eyes are clearly observed.](NFT_eyeselection.png "wikilink")
 
-</center>
-<center>
 
 ![Figure 7: Matlab figure to click on eye lobes.](NFT_eyelobes.png "wikilink")
 
-</center>
-
 ### Inner skull segmentation
 
 Inner skull segmentation does not require any user input. After the
@@ -188,13 +172,10 @@ mesh file is suitable to be used directly by the BEM solver. The format
 of the mesh file is given in [Appendix A](/NFT_Appendix_A "wikilink").
 The mesh generation process is described below.
 
-<center>
-
 ![](NFT_from_MRI_mesh_gen.png "wikilink") .....
 ![Figure 8: Interface for mesh
 generation.](NFT_meshgeneration_ui.png "wikilink")
 
-</center>
 
 Mesh Generation module creates triangular meshes that fits the
 boundaries of the segmentation. The aim is to approximate the geometry
@@ -246,13 +227,9 @@ A LFM using a regular grid source space can be used in single dipole
 parametric inverse problem solution to find a coarse estimate of the
 dipole position.
 
-<center>
-
 ![](NFT_from_MRI_source_space.png "wikilink") ..... 
 ![Figure 9: Interface for source space generation.](NFT_sourcespacegen.png "wikilink")
 
-</center>
-
 Co-registration of electrode locations
 --------------------------------------
 
@@ -287,12 +264,9 @@ the electrode positions change. Therefore, the co-registration output is
 specific to a session. The result of electrode co-registration is saved
 as Session_Name_Subject_Name_headsensors.sens in ASCII format.
 
-<center>
-
 ![](NFT_from_MRI_coreg.png "wikilink") .... 
 ![Figure 10: Interface for co-registration.](NFT_coregistration.png "wikilink")
 
-</center>
 
 Head Modeling using Template Warping
 ------------------------------------
@@ -332,13 +306,9 @@ whole head model, and some electrodes may fall out of the template mesh.
 
 In Figure 11 the interface for warping module is shown.
 
-<center>
-
 ![](NFT_from_Warping.png "wikilink") ..... 
 ![Figure 11: Interface warping of a template head model.](NFT_warping_ui.png "wikilink")
 
-</center>
-
 ------------------------------------------------------------------------
 
 References

plugins/NFT/Chapter_03_Forward_Model_Generation.md → plugins/NFT/Chapter-03-Forward-Model-Generation.md

@@ -1,9 +1,11 @@
 ---
 layout: default
-title: NFT
-long_title: NFT
 parent: NFT
 grand_parent: Plugins
+render_with_liquid: false
+
+title: Chapter-03-Forward-Model-Generation
+long_title: Chapter-03-Forward-Model-Generation
 ---
 Forward Problem: Boundary Element Method
 ----------------------------------------

plugins/NFT/Chapter_04_NFT_Examples.md → plugins/NFT/Chapter-04-NFT-Examples.md

@@ -1,9 +1,11 @@
 ---
 layout: default
-title: NFT
-long_title: NFT
 parent: NFT
 grand_parent: Plugins
+render_with_liquid: false
+
+title: Chapter-04-NFT-Examples
+long_title: Chapter-04-NFT-Examples
 ---
 In this section, two head modeling examples are presented. Both of these
 examples use the same subject. The first example generates a realistic
@@ -21,8 +23,6 @@ through segmentation and mesh generation steps. The mesh consists of
 scalp, skull, csf, and brain layers for a total of 16016 nodes and 32024
 elements. The individual layers can be seen in Figure 13.
 
-<center>
-
 (a) 
 ![a](NFM_Toolboox_UsersManual_html_56c540a1.gif "wikilink") ...
 (b)
@@ -32,29 +32,21 @@ elements. The individual layers can be seen in Figure 13.
 (d)
 ![d](NFM_Toolboox_UsersManual_html_m69f7a676.gif "wikilink")
 
-</center>
-
 Figure 13: BEM model of the scalp, skull, csf and the brain obtained
 from an MR image. (a) scalp mesh, (b) skull mesh, (c) CSF mesh, (d)
 brain mesh.
 
 After mesh generation, the electrodes and the realistic mesh is
 co-registered. The result of co-registration can be seed in Figure 14.
 
-<center>
-
 ![Figure 14: Registered electrode locations on the scalp mesh.](NFM_Toolboox_UsersManual_html_7b73089f.gif "wikilink")
 
-</center>
-
 The second example assumes that the only available subject data is the
 141 digitized electrode locations. For warping the template MNI mesh is
 used, which has three layers and 3000 nodes and 5988 elements. This is
 the standard mesh that is also used by other BEM solvers in the
 literature. The results of warping can be seen in Figure 15.
 
-<center>
-
 (a)
 ![a](NFM_Toolboox_UsersManual_html_3bc436a3.gif "wikilink") ...
 (b) 
@@ -64,8 +56,6 @@ literature. The results of warping can be seen in Figure 15.
 (d) 
 ![d](NFM_Toolboox_UsersManual_html_m788a9795.gif "wikilink")
 
-</center>
-
 Figure 15: BEM model of the scalp, skull, the brain obtained by warping
 a template head model to electrode locations. (a) scalp mesh, (b) skull
 mesh, (c) brain mesh, (d) electrode locations.

plugins/NFT/Chapter_05_NFT_Commands_and_Functions.md → plugins/NFT/Chapter-05-NFT-Commands-and-Functions.md

@@ -1,9 +1,11 @@
 ---
 layout: default
-title: NFT
-long_title: NFT
 parent: NFT
 grand_parent: Plugins
+render_with_liquid: false
+
+title: Chapter-05-NFT-Commands-and-Functions
+long_title: Chapter-05-NFT-Commands-and-Functions
 ---
 This section summarizes the MATLAB commands and data structures used for
 each stage of head modeling using the NFT toolbox. The function

plugins/NFT/NFT_Appendix_A.md → plugins/NFT/NFT-Appendix-A.md

@@ -1,9 +1,11 @@
 ---
 layout: default
-title: NFT
-long_title: NFT
 parent: NFT
 grand_parent: Plugins
+render_with_liquid: false
+
+title: NFT-Appendix-A
+long_title: NFT-Appendix-A
 ---
 BEM Mesh Format
 ---------------