Some trials of algorithms for making an initial Chest Wall contour on the Mid-Sagittal slice.
A manual rectangular seed placed and EdgeWave ran with parameters SiLo\SiHi = {0.5,2} Peel\Grow = {5,10}mm
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Saturday, December 3, 2016
Monday, November 21, 2016
MapFit\Breast segmentation
Implemented a dual-smoothness concept:
a) Deformation field is smooth so there is a separate smoothing procedure for that
with the constant organ-independent parameters (no UI-parameters).
b) Organ boundary smoothness: this is the constrained and is smoothed separately. User sets the parameters defining the smoothness of the resulting organ contour.
Thursday, November 3, 2016
Tuesday, October 25, 2016
Wednesday, October 19, 2016
Thursday, October 6, 2016
Tuesday, October 4, 2016
Monday, October 3, 2016
Monday, September 26, 2016
Thursday, September 22, 2016
Tuesday, September 20, 2016
Whole Breast segmentation: one idea is to forgo the Chest Wall Segmentation (CWS) and go directly to Whole Breast Segmentation.
One very quick prototype is based in the old algorithm principally based on the pure morphology. Of course there are many details to figure out.
Case is "DASS T1-nonFS"
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| Selected slices for the WBS |
Whole Breast segmentation: one idea is to forgo the Chest Wall Segmentation (CWS) and go directly to Whole Breast Segmentation.
One very quick prototype is based in the old algorithm principally based on the pure morphology. Of course there are many details to figure out.
Case is "DASS T1-nonFS"
![]() |
| Selected slices for the WBS |
Friday, September 16, 2016
Thursday, September 15, 2016
Wednesday, September 14, 2016
Tuesday, September 13, 2016
EdgeDetector 3D: greatly reduced the memory requirements.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradient Map which is extremely memory consuming.
Also, this function is applicable throughout FireVoxel where only the Edge Magnitude map or Binary Edge are sufficient.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradient Map which is extremely memory consuming.
Also, this function is applicable throughout FireVoxel where only the Edge Magnitude map or Binary Edge are sufficient.
EdgeDetector 3D: greatly reduced the memory requirements.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradien Map which is extremely memory consuming.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradien Map which is extremely memory consuming.
Sunday, September 11, 2016
Thursday, September 8, 2016
Saturday, September 3, 2016
Friday, September 2, 2016
Thursday, September 1, 2016
Wednesday, August 31, 2016
Saturday, August 27, 2016
Rat Brain registration: resolved the quality problem by adding and additional (5th) landmark at the back of the brain in the Eyes Plain. Apparently the 4 Landmark configuration was too close to a Coplanar that was causing the Affine registration to fail.
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| Additional BrainBack landmark |
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| Affine registration result: Transformed CT overlaid by the MR-template landmarks. |
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| CT layer (Green) overlaid with transparency on top of the MR layer (Red) |
Thursday, August 25, 2016
BiCal Smooth preprocess: Changed the algorithm and the User Interface item to a simple checkbox (ON\OFF). This finally makes BiCal with all parameter Quasi-constants. I.e. User can run the BiCal on all volumes with very good result without changing parameter. The eliminated SmoothPower parameter was extremely challenging to replace.
Friday, August 19, 2016
Thursday, August 18, 2016
Tuesday, August 16, 2016
Edge-Constrained volume smooth (sometimes called ObjectSmooth or QuasiSegmentation in FireVoxel): implemented a Zero-Edge variant when the thickness of the edges is 0 instead of current 1-voxel. This allows not to use artificially higher resolution for better result.
This is one of the principal components of the BiCal. But also it has a big potential to improve other operations, possibly MagTrace, and as as the pre-processing for more complex segmentations.
![]() |
| Smoothed power=80 |
Saturday, August 6, 2016
BiCal functionality:
"Low signal level" parameter has replaced the "Air Threshold" parameter. The new parameter is quasi-constant ( in [5,10] range) and is independent of the volume content.
Separated the notion of Air Threshold and the Signal-to-Gradient ration. They produce separate masks now. Air-Threshold can be defined very approximately now and completely automatically without User Input, as it is mostly used cosmetically to make the resulting volume to look without Air amplification. Another component, the areas of the low Signal-to-Gradient ratio are excluded and this improves the Non-Uniformity measures substantially.
For the reference image, BPD-7T, with the same parameters the measures for
Build 223: WM\GM\CSM improved from Build 224: 0.14\0.16\0.045 down to 0.07\0.14\0.031
"Low signal level" parameter has replaced the "Air Threshold" parameter. The new parameter is quasi-constant ( in [5,10] range) and is independent of the volume content.
Separated the notion of Air Threshold and the Signal-to-Gradient ration. They produce separate masks now. Air-Threshold can be defined very approximately now and completely automatically without User Input, as it is mostly used cosmetically to make the resulting volume to look without Air amplification. Another component, the areas of the low Signal-to-Gradient ratio are excluded and this improves the Non-Uniformity measures substantially.
For the reference image, BPD-7T, with the same parameters the measures for
Build 223: WM\GM\CSM improved from Build 224: 0.14\0.16\0.045 down to 0.07\0.14\0.031
![]() |
| ORIGINAL |
![]() |
| After BICAL |
Wednesday, August 3, 2016
Tuesday, August 2, 2016
Monday, August 1, 2016
BiCal profiling result (see diagram below):
1. Starts with Edge detection - virtually 100% parallel.
2. Then processing start and separate peaks corresponds to iterations
with the increasing degree of Legendre polynomials.
3. As degree grows, the SVD (= fully parallel) portion grows so the total
core load grows too.
I will be addressing the non-parallelized part discovered with VC++ Profiler.
1. Starts with Edge detection - virtually 100% parallel.
2. Then processing start and separate peaks corresponds to iterations
with the increasing degree of Legendre polynomials.
3. As degree grows, the SVD (= fully parallel) portion grows so the total
core load grows too.
I will be addressing the non-parallelized part discovered with VC++ Profiler.
Sunday, July 31, 2016
Saturday, July 30, 2016
Wednesday, July 27, 2016
Major improvements to BiCal using new SVD-Parallel Jacobi:
a) Was able to process much higher degrees of the polynomial - 25 from previous max 15. New
image looks great and better. Not able to do it in previous versions due to numerical instability of the
20000x20000 matrix solution using old SVD-NumericalRecipes (QR-decomposition). Jacobi algorithm is simpler but more stable.
b) At Degree=10 SVD takes 1% of calculation time, at 25 degree it takes 99% of calculation time.
But now SVD is 100% parallel and scalable with 10x faster than previous version.
c) On BPD datasets, results for WM/GM/CSM improved from 0.14/0.14/0.064 down to 0.10/0.11/0.49 for degree 25.
d) But calculation times become prohibitive at the higher degress ~40 min.
a) Was able to process much higher degrees of the polynomial - 25 from previous max 15. New
image looks great and better. Not able to do it in previous versions due to numerical instability of the
20000x20000 matrix solution using old SVD-NumericalRecipes (QR-decomposition). Jacobi algorithm is simpler but more stable.
b) At Degree=10 SVD takes 1% of calculation time, at 25 degree it takes 99% of calculation time.
But now SVD is 100% parallel and scalable with 10x faster than previous version.
c) On BPD datasets, results for WM/GM/CSM improved from 0.14/0.14/0.064 down to 0.10/0.11/0.49 for degree 25.
d) But calculation times become prohibitive at the higher degress ~40 min.
Tuesday, July 26, 2016
Thursday, July 21, 2016
Monday, July 18, 2016
Sunday, July 17, 2016
Monday, July 11, 2016
Friday, July 8, 2016
Added new Dynamic Model: "DDC Stretched Exponential fit S = S0*(exp(-b*ADC)^alpha)"
Tested on the standard ABT dataset identical to ADC:
For comparison, we performed the ADC fit on the same data:
Corresponding Alpha-map processed over the ROI
Tested on the standard ABT dataset identical to ADC:
![]() |
| DDC fit: DDC=0.0016, alpha=0.34, Residual=2.03 |
![]() |
| ADC fit: ADC=0.001152, Residual=12 |
Corresponding Alpha-map processed over the ROI
Thursday, July 7, 2016
Wednesday, June 22, 2016
Saturday, June 18, 2016
Wednesday, June 15, 2016
Tuesday, June 14, 2016
Friday, June 10, 2016
Tuesday, June 7, 2016
Saturday, June 4, 2016
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