Code improvement:  moved the CDlgLayerCtrl declaration within the class source module.

Dynamic Modelling module:  implemented the Parametric map "Input function correlation"

Femur ROI registratio: Implemented a dedicated menu item with parameters suitable for this application.  Default parameters provide speed of under 5 min on the WALS1-WALS2 sample.
Speed is variable and defined by the precision of the AutoFocus stage (such as rotation precision and range)

ROI->ROI registration:  during the reslicing operation in AutoFocus\Finetune iteration ROIs are transformed and partial volume voxels appear while the majority of voxels still remain binary ROI. Calculating of signal difference in the obvious way was not fast enough for comparing the massive images for Femur registration problem.
Developed the Signal difference calculation working directly with Run-length representation of ROIs (x10-20 memory compact) that resulted in x10 speed up during the AutoFocus on Subscale=3. Speed up is even higher on the Finetune phase approaching x30.

Femur registration of Bone ROI->ROI using "Signal Difference" Measure.  Matching result is good but takes more than 1hr.

Build 205 is released

"MainMenu>ROI>Split ROI by Threshold" - added the BiModal Laplacian with ration method for initial split.

Enabled accelerator "Ctrl+S" :  "Save FireVoxel document"

Implemented new rule to select threshold from 2 modelled distribution:  "Minimize Missclassification" rule.

BiModal Laplacian histogram

CT-PET-ATLAS workflow: Register ALL:  fixed the crash defect on case W73"Deep breathing" lung segmentation case:  no leaks after BiCal+EdgeWave

Mouse Brain, CT-PET-ATLAS wokflow:
Provided an additional registration function to register everything to the PET space to avoid any transforms\interpolation of the principal data (PET).

Mouse Brain   CT-PET-Atlas workfow:  finished the integrated "one-click" registration function

Added the functionality to extract only desired regions from the Atlas

Landmark co-registration:
a) When Source={3D,4D} 2-layer entity, registered to a 3D-volume, the timing information of the 4D volume was lost
b) Also corrected the defect when Alpha (transparency) and the color scheme of the source were lost after the regidstration.

Dialog RoiStats3D:  in Model-based Histogram segmentation added an option to view the "Modelling Cumulative" curve  (options are {None,All Curves,Cumulative"}
Also removed the "BiGauss Explore" and "BiGauss with Ratio Explore" options since they are superceded by the curves display now.

Histogram model-based segmentation {BiGauss,BiLaplace}: Implemented initial framework to show the modelling curves overlaid on top of the histogram

Implemented some improvements to Global Optimization algorithm and code.

Fixed crash defect while loading the DICOM folder obtained from Mr. Zhang under Win10. This was due to using the 32-bit truncation in CTreeCtrl::SetItemData during the DICOM tree construction.

Moved the main development environment to Windows 10.

Fixed the defect in RasterPaintbrush dialog box:  checking the "Allow paint on parametric Maps" had no effect and setting was not remembered.  Drawing on Parametric maps is now possible.

Fixed the paintbrush problem when drawing on integer volumes.

Registration by the landmarks:  provided more detailed analysis in case of the landmark mismatch between Source and Target.  

Registration by Landmarks:  upon the start of the procedure all the "invisible" (void) landmarks are unconditionally removed from ALL documents. This is to avoid frequent confusion during the registration.

"MainMenu>ROI>Split ROI by Threshold" - added the BiModal Laplacian with ration method for initial split.

Enabled accelerator "Ctrl+S" :  "Save FireVoxel document"

Implemented new rule to select threshold from 2 modelled distribution:  "Minimize Missclassification" rule. This rule does not use actual histogram values

Further extensions are possible with histogram value usage.

We have found good BiModal match of the Histogram.  Having two curves (Gaussian or Laplacian) what is the suitable algorithm to choose the threshold?  Presently we choose the lowest point between the peaks of two components, but now I have doubts about this.  Should it be the intersection of 2 curves instead?

BiModal Laplacian histogram segmentation (first result):

This is simplistic global optimization (without extremum checking so did not perform as well as BiGaussian with the same number of iterations = 200K)

Proposed semi-automatic "breast\chest wall" segmentation:

One way is to segment OUT the chest wall, after that breast segmentation is a simple EdgeWave operation.

To segment the chest wall:
1. On every 5th slice, draw the chestwall boundary as an ROI (see pic below).  In this prototype I recommend the boundary starting and ending at the margins of the image (later on we can eliminate this).

2. I will provide a specialized operation "MainMenu>Applications>Breast>Segment Chest wall from Contours".  Internally this operation would perform completing the contour, and then "Fill & Morph convex" to fill the skipped slices.

3.  Result of this operation would be a "CHEST ROI" that could be excluded from the image.  Then we would simply apply the EdgeWave to segment out the Air.

4. Manual processing time should be <1 min.  Computing time is <20 sec.

CT-PET-ATLAS workflow: Register ALL:  fixed the crash defect on case W73

"Deep breathing" lung segmentation case:  no leaks after BiCal+EdgeWave

Some very approximate parameters for BiCal - would work in the wide range.

BiCal parameters

EdgeWave (with seed) parameters

Mouse Brain, CT-PET-ATLAS wokflow:

Provided an additional registration function to register everything to the PET space to avoid any transforms\interpolation of the principal data (PET).

Mouse Brain,  CT-PET-Atlas workflow.



1. Load Atlas once for the whole study session. Minimize the View, so it is out the way and reduce screen clutter. You don't need to open Atlas for every registration.

2.  Load CT-3D volume with landmarks present

3.  Load PET-4D volulume

(steps 2,3 in any order)

4.  Choose "MainMenu>Applications>Mouse Brain Atlas-PET-CT workflow"

5.  After brief processing only one document will remain that contains
CT,PET and several ROI layers.

6.  Press F4 to bring up the curve dialog, this might take up to 1 min since there are many ROIs present.  You will see something like below:

Mouse Brain   CT-PET-Atlas workfow:  finished the integrated "one-click" registration function.  Resulting entity is ready to get the PET curves and save them (to text file)

Prepared first integrated mouse brain Atlas with ROI regions named and the 4 Landmarks included.  This is ready for the final processing.

Added the functionality to extract only desired regions from the Atlas.  User enters the desired region indices separated by commas.  Result: they will appear as the new ROI layes named "atlas region #x" according to the specified indices.

Landmark co-registration:
a) When Source={3D,4D} 2-layer entity, registered to a 3D-volume, the timing information of the 4D volume was lost
b) Also corrected the defect when Alpha (transparency) and the color scheme of the source were lost after the regidstration.