/
N3BiasFieldCorrection.cxx
1161 lines (1017 loc) · 43.1 KB
/
N3BiasFieldCorrection.cxx
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#include "antsUtilities.h"
#include "antsAllocImage.h"
#include "antsCommandLineParser.h"
#include "ReadWriteData.h"
#include "itkBinaryThresholdImageFilter.h"
#include "itkBSplineControlPointImageFilter.h"
#include "itkConstantPadImageFilter.h"
#include "itkExpImageFilter.h"
#include "itkExtractImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkImageRegionIterator.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "itkLabelStatisticsImageFilter.h"
#include "itkN3MRIBiasFieldCorrectionImageFilter.h"
#include "itkN3BiasFieldCorrectionImageFilter.h"
#include "itkOtsuThresholdImageFilter.h"
#include "itkTimeProbe.h"
#include "itkShrinkImageFilter.h"
#include <string>
#include <algorithm>
#include <vector>
#include "ANTsVersion.h"
namespace ants
{
template <typename TFilter>
class CommandIterationUpdate final : public itk::Command
{
public:
using Self = CommandIterationUpdate<TFilter>;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
const auto * filter = dynamic_cast<const TFilter *>(object);
if (typeid(event) != typeid(itk::IterationEvent))
{
return;
}
std::cout << "Iteration " << filter->GetElapsedIterations() << " (of " << filter->GetMaximumNumberOfIterations()
<< "). ";
std::cout << " Current convergence value = " << filter->GetCurrentConvergenceMeasurement()
<< " (threshold = " << filter->GetConvergenceThreshold() << ")" << std::endl;
}
};
template <unsigned int ImageDimension>
int
N3BiasFieldCorrection(int argc, char * argv[])
{
using RealType = float;
using ImageType = itk::Image<RealType, ImageDimension>;
using MaskImageType = itk::Image<unsigned char, ImageDimension>;
typename ImageType::Pointer image;
ReadImage<ImageType>(image, argv[2]);
using ShrinkerType = itk::ShrinkImageFilter<ImageType, ImageType>;
typename ShrinkerType::Pointer shrinker = ShrinkerType::New();
shrinker->SetInput(image);
shrinker->SetShrinkFactors(1);
typename MaskImageType::Pointer maskImage = nullptr;
if (argc > 5)
{
ReadImage<MaskImageType>(maskImage, argv[5]);
}
if (!maskImage)
{
using ThresholderType = itk::OtsuThresholdImageFilter<ImageType, MaskImageType>;
typename ThresholderType::Pointer otsu = ThresholderType::New();
otsu->SetInput(image);
otsu->SetNumberOfHistogramBins(200);
otsu->SetInsideValue(0);
otsu->SetOutsideValue(1);
otsu->Update();
maskImage = otsu->GetOutput();
}
using MaskShrinkerType = itk::ShrinkImageFilter<MaskImageType, MaskImageType>;
typename MaskShrinkerType::Pointer maskshrinker = MaskShrinkerType::New();
maskshrinker->SetInput(maskImage);
maskshrinker->SetShrinkFactors(1);
// 4 is [shrinkFactor]
// 5 is [maskImage]
if (argc > 4)
{
shrinker->SetShrinkFactors(std::stoi(argv[4]));
maskshrinker->SetShrinkFactors(std::stoi(argv[4]));
}
shrinker->Update();
maskshrinker->Update();
using CorrecterType = itk::N3MRIBiasFieldCorrectionImageFilter<ImageType, MaskImageType, ImageType>;
typename CorrecterType::Pointer correcter = CorrecterType::New();
correcter->SetInput(shrinker->GetOutput());
correcter->SetMaskImage(maskshrinker->GetOutput());
// 6 is [numberOfIterations]
if (argc > 6)
{
correcter->SetMaximumNumberOfIterations(std::stoi(argv[6]));
}
// 7 is [numberOfFittingLevels]
if (argc > 7)
{
correcter->SetNumberOfFittingLevels(std::stoi(argv[7]));
}
// 8 is [outputBiasField]
// 9 is [verbose]
bool verbose = false;
if (argc > 9)
{
verbose = static_cast<bool>(std::stoi(argv[9]));
}
using CommandType = CommandIterationUpdate<CorrecterType>;
typename CommandType::Pointer observer = CommandType::New();
if (verbose)
{
correcter->AddObserver(itk::IterationEvent(), observer);
}
try
{
correcter->Update();
}
catch (...)
{
std::cout << "Exception caught." << std::endl;
return EXIT_FAILURE;
}
// correcter->Print( std::cout, 3 );
/**
* Reconstruct the bias field at full image resolution. Divide
* the original input image by the bias field to get the final
* corrected image.
*/
using BSplinerType = itk::BSplineControlPointImageFilter<typename CorrecterType::BiasFieldControlPointLatticeType,
typename CorrecterType::ScalarImageType>;
typename BSplinerType::Pointer bspliner = BSplinerType::New();
bspliner->SetInput(correcter->GetLogBiasFieldControlPointLattice());
bspliner->SetSplineOrder(correcter->GetSplineOrder());
bspliner->SetSize(image->GetLargestPossibleRegion().GetSize());
bspliner->SetOrigin(image->GetOrigin());
bspliner->SetDirection(image->GetDirection());
bspliner->SetSpacing(image->GetSpacing());
bspliner->Update();
typename ImageType::Pointer logField = AllocImage<ImageType>(bspliner->GetOutput());
itk::ImageRegionIterator<typename CorrecterType::ScalarImageType> ItB(
bspliner->GetOutput(), bspliner->GetOutput()->GetLargestPossibleRegion());
itk::ImageRegionIterator<ImageType> ItF(logField, logField->GetLargestPossibleRegion());
for (ItB.GoToBegin(), ItF.GoToBegin(); !ItB.IsAtEnd(); ++ItB, ++ItF)
{
ItF.Set(ItB.Get()[0]);
}
using ExpFilterType = itk::ExpImageFilter<ImageType, ImageType>;
typename ExpFilterType::Pointer expFilter = ExpFilterType::New();
expFilter->SetInput(logField);
expFilter->Update();
using DividerType = itk::DivideImageFilter<ImageType, ImageType, ImageType>;
typename DividerType::Pointer divider = DividerType::New();
divider->SetInput1(image);
divider->SetInput2(expFilter->GetOutput());
divider->Update();
using WriterType = itk::ImageFileWriter<ImageType>;
typename WriterType::Pointer writer = WriterType::New();
if (argc < 4)
{
std::cout << "missing divider image filename" << std::endl;
throw;
}
ANTs::WriteImage<ImageType>(divider->GetOutput(), argv[3]);
if (argc > 8)
{
ANTs::WriteImage<ImageType>(expFilter->GetOutput(), argv[8]);
}
return EXIT_SUCCESS;
}
template <unsigned int ImageDimension>
int
N3(itk::ants::CommandLineParser * parser)
{
using RealType = float;
using ImageType = itk::Image<RealType, ImageDimension>;
typename ImageType::Pointer inputImage = nullptr;
using MaskImageType = itk::Image<RealType, ImageDimension>;
typename MaskImageType::Pointer maskImage = nullptr;
bool verbose = false;
typename itk::ants::CommandLineParser::OptionType::Pointer verboseOption = parser->GetOption("verbose");
if (verboseOption && verboseOption->GetNumberOfFunctions())
{
verbose = parser->Convert<bool>(verboseOption->GetFunction(0)->GetName());
}
if (verbose)
{
std::cout << std::endl << "Running N3 for " << ImageDimension << "-dimensional images." << std::endl << std::endl;
}
using CorrecterType = itk::N3BiasFieldCorrectionImageFilter<ImageType, MaskImageType, ImageType>;
typename CorrecterType::Pointer correcter = CorrecterType::New();
typename itk::ants::CommandLineParser::OptionType::Pointer inputImageOption = parser->GetOption("input-image");
if (inputImageOption && inputImageOption->GetNumberOfFunctions())
{
std::string inputFile = inputImageOption->GetFunction(0)->GetName();
ReadImage<ImageType>(inputImage, inputFile.c_str());
}
else
{
if (verbose)
{
std::cerr << "Input image not specified." << std::endl;
}
return EXIT_FAILURE;
}
/**
* handle the mask image
*/
bool isMaskImageSpecified = false;
typename itk::ants::CommandLineParser::OptionType::Pointer maskImageOption = parser->GetOption("mask-image");
if (maskImageOption && maskImageOption->GetNumberOfFunctions())
{
std::string inputMaskFile = maskImageOption->GetFunction(0)->GetName();
ReadImage<MaskImageType>(maskImage, inputMaskFile.c_str());
isMaskImageSpecified = true;
}
if (maskImage.IsNull())
{
if (verbose)
{
std::cout << "Mask not read. Using the entire image as the mask." << std::endl << std::endl;
}
maskImage = MaskImageType::New();
maskImage->CopyInformation(inputImage);
maskImage->SetRegions(inputImage->GetRequestedRegion());
maskImage->Allocate();
maskImage->FillBuffer(itk::NumericTraits<typename MaskImageType::PixelType>::OneValue());
}
/**
* check for negative values in the masked region
*/
using ThresholderType = itk::BinaryThresholdImageFilter<MaskImageType, MaskImageType>;
typename ThresholderType::Pointer thresholder = ThresholderType::New();
thresholder->SetInsideValue(itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue());
thresholder->SetOutsideValue(itk::NumericTraits<typename MaskImageType::PixelType>::OneValue());
thresholder->SetLowerThreshold(itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue());
thresholder->SetUpperThreshold(itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue());
thresholder->SetInput(maskImage);
using StatsType = itk::LabelStatisticsImageFilter<ImageType, MaskImageType>;
typename StatsType::Pointer statsOriginal = StatsType::New();
statsOriginal->SetInput(inputImage);
statsOriginal->SetLabelInput(thresholder->GetOutput());
statsOriginal->UseHistogramsOff();
statsOriginal->Update();
using StatsLabelType = typename StatsType::LabelPixelType;
StatsLabelType maskLabel = itk::NumericTraits<StatsLabelType>::OneValue();
RealType minOriginal = statsOriginal->GetMinimum(maskLabel);
RealType maxOriginal = statsOriginal->GetMaximum(maskLabel);
if (verbose)
{
std::cout << "Original intensity range: [" << minOriginal << ", " << maxOriginal << "]" << std::endl;
}
if (minOriginal <= 0)
{
if (verbose)
{
std::cout << std::endl;
std::cout << "***********************************************************" << std::endl;
std::cout << "Warning: Your input image contains nonpositive values" << std::endl;
std::cout << "which could cause failure or problematic results. A" << std::endl;
std::cout << "possible workaround would be to:" << std::endl;
std::cout << " 1. rescale your image to positive values e.g., [10,100]." << std::endl;
std::cout << " 2. run N3 on your rescaled image." << std::endl;
std::cout << " 3. (optional) rescale the N3 output to the original" << std::endl;
std::cout << " intensity range." << std::endl;
std::cout << "***********************************************************" << std::endl;
std::cout << std::endl;
}
}
/**
* handle the weight image
*/
typename ImageType::Pointer weightImage = nullptr;
typename itk::ants::CommandLineParser::OptionType::Pointer weightImageOption = parser->GetOption("weight-image");
if (weightImageOption && weightImageOption->GetNumberOfFunctions())
{
std::string inputFile = weightImageOption->GetFunction(0)->GetName();
ReadImage<ImageType>(weightImage, inputFile.c_str());
}
/**
* convergence options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer convergenceOption = parser->GetOption("convergence");
if (convergenceOption && convergenceOption->GetNumberOfFunctions())
{
if (convergenceOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
unsigned int maximumNumberOfIterations =
parser->Convert<unsigned int>(convergenceOption->GetFunction(0)->GetParameter(0));
correcter->SetMaximumNumberOfIterations(maximumNumberOfIterations);
correcter->SetConvergenceThreshold(0.0);
}
if (convergenceOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
correcter->SetConvergenceThreshold(parser->Convert<float>(convergenceOption->GetFunction(0)->GetParameter(1)));
}
}
else // set default values
{
correcter->SetMaximumNumberOfIterations(50);
correcter->SetConvergenceThreshold(0.0);
}
/**
* B-spline options -- we place this here to take care of the case where
* the user wants to specify things in terms of the spline distance.
*/
typename ImageType::IndexType inputImageIndex = inputImage->GetLargestPossibleRegion().GetIndex();
typename ImageType::SizeType inputImageSize = inputImage->GetLargestPossibleRegion().GetSize();
typename ImageType::PointType newOrigin = inputImage->GetOrigin();
typename itk::ants::CommandLineParser::OptionType::Pointer bsplineOption = parser->GetOption("bspline-fitting");
if (bsplineOption && bsplineOption->GetNumberOfFunctions())
{
if (bsplineOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
correcter->SetSplineOrder(parser->Convert<unsigned int>(bsplineOption->GetFunction(0)->GetParameter(2)));
}
if (bsplineOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
correcter->SetNumberOfFittingLevels(
parser->Convert<unsigned int>(bsplineOption->GetFunction(0)->GetParameter(1)));
}
if (bsplineOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
std::vector<float> array = parser->ConvertVector<float>(bsplineOption->GetFunction(0)->GetParameter(0));
typename CorrecterType::ArrayType numberOfControlPoints;
if (array.size() == 1)
{
// the user wants to specify things in terms of spline distance.
// 1. need to pad the images to get as close to possible to the
// requested domain size.
float splineDistance = array[0];
typename ImageType::SizeType originalImageSize = inputImage->GetLargestPossibleRegion().GetSize();
itk::Size<ImageDimension> lowerBound;
itk::Size<ImageDimension> upperBound;
for (unsigned int d = 0; d < ImageDimension; d++)
{
float domain = static_cast<float>(originalImageSize[d] - 1) * static_cast<float>(inputImage->GetSpacing()[d]);
auto numberOfSpans = static_cast<unsigned int>(std::ceil(domain / splineDistance));
auto extraPadding = static_cast<unsigned long>((numberOfSpans * splineDistance - domain) /
static_cast<float>(inputImage->GetSpacing()[d]) +
static_cast<float>(0.5));
lowerBound[d] = static_cast<unsigned long>(0.5 * extraPadding);
upperBound[d] = extraPadding - lowerBound[d];
newOrigin[d] -= (static_cast<double>(lowerBound[d]) * inputImage->GetSpacing()[d]);
numberOfControlPoints[d] = numberOfSpans + correcter->GetSplineOrder();
}
using PadderType = itk::ConstantPadImageFilter<ImageType, ImageType>;
typename PadderType::Pointer padder = PadderType::New();
padder->SetInput(inputImage);
padder->SetPadLowerBound(lowerBound);
padder->SetPadUpperBound(upperBound);
padder->SetConstant(itk::NumericTraits<typename ImageType::PixelType>::ZeroValue());
padder->Update();
inputImage = padder->GetOutput();
inputImage->DisconnectPipeline();
using MaskPadderType = itk::ConstantPadImageFilter<MaskImageType, MaskImageType>;
typename MaskPadderType::Pointer maskPadder = MaskPadderType::New();
maskPadder->SetInput(maskImage);
maskPadder->SetPadLowerBound(lowerBound);
maskPadder->SetPadUpperBound(upperBound);
maskPadder->SetConstant(0);
maskPadder->Update();
maskImage = maskPadder->GetOutput();
maskImage->DisconnectPipeline();
if (weightImage)
{
typename PadderType::Pointer weightPadder = PadderType::New();
weightPadder->SetInput(weightImage);
weightPadder->SetPadLowerBound(lowerBound);
weightPadder->SetPadUpperBound(upperBound);
weightPadder->SetConstant(0);
weightPadder->Update();
weightImage = weightPadder->GetOutput();
weightImage->DisconnectPipeline();
}
if (verbose)
{
std::cout << "Specified spline distance: " << splineDistance << "mm" << std::endl;
std::cout << " original image size: " << originalImageSize << std::endl;
std::cout << " padded image size: " << inputImage->GetLargestPossibleRegion().GetSize() << std::endl;
std::cout << " number of control points: " << numberOfControlPoints << std::endl;
std::cout << std::endl;
}
}
else if (array.size() == ImageDimension)
{
for (unsigned int d = 0; d < ImageDimension; d++)
{
numberOfControlPoints[d] = static_cast<unsigned int>(array[d]) + correcter->GetSplineOrder();
}
}
else
{
if (verbose)
{
std::cerr << "Incorrect mesh resolution" << std::endl;
}
return EXIT_FAILURE;
}
correcter->SetNumberOfControlPoints(numberOfControlPoints);
}
}
using ShrinkerType = itk::ShrinkImageFilter<ImageType, ImageType>;
typename ShrinkerType::Pointer shrinker = ShrinkerType::New();
shrinker->SetInput(inputImage);
shrinker->SetShrinkFactors(1);
using MaskShrinkerType = itk::ShrinkImageFilter<MaskImageType, MaskImageType>;
typename MaskShrinkerType::Pointer maskshrinker = MaskShrinkerType::New();
maskshrinker->SetInput(maskImage);
maskshrinker->SetShrinkFactors(1);
typename itk::ants::CommandLineParser::OptionType::Pointer shrinkFactorOption = parser->GetOption("shrink-factor");
int shrinkFactor = 4;
if (shrinkFactorOption && shrinkFactorOption->GetNumberOfFunctions())
{
shrinkFactor = parser->Convert<int>(shrinkFactorOption->GetFunction(0)->GetName());
}
shrinker->SetShrinkFactors(shrinkFactor);
maskshrinker->SetShrinkFactors(shrinkFactor);
if (ImageDimension == 4)
{
shrinker->SetShrinkFactor(3, 1);
maskshrinker->SetShrinkFactor(3, 1);
}
shrinker->Update();
maskshrinker->Update();
itk::TimeProbe timer;
timer.Start();
correcter->SetInput(shrinker->GetOutput());
correcter->SetMaskImage(maskshrinker->GetOutput());
using WeightShrinkerType = itk::ShrinkImageFilter<ImageType, ImageType>;
typename WeightShrinkerType::Pointer weightshrinker = WeightShrinkerType::New();
if (weightImage)
{
weightshrinker->SetInput(weightImage);
weightshrinker->SetShrinkFactors(shrinker->GetShrinkFactors());
weightshrinker->Update();
correcter->SetConfidenceImage(weightshrinker->GetOutput());
}
if (verbose)
{
using CommandType = CommandIterationUpdate<CorrecterType>;
typename CommandType::Pointer observer = CommandType::New();
correcter->AddObserver(itk::IterationEvent(), observer);
}
/**
* histogram sharpening options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer histOption = parser->GetOption("histogram-sharpening");
if (histOption && histOption->GetNumberOfFunctions())
{
if (histOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
correcter->SetBiasFieldFullWidthAtHalfMaximum(
parser->Convert<float>(histOption->GetFunction(0)->GetParameter(0)));
}
if (histOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
correcter->SetWienerFilterNoise(parser->Convert<float>(histOption->GetFunction(0)->GetParameter(1)));
}
if (histOption->GetFunction(0)->GetNumberOfParameters() > 2)
{
correcter->SetNumberOfHistogramBins(parser->Convert<unsigned int>(histOption->GetFunction(0)->GetParameter(2)));
}
}
try
{
// correcter->DebugOn();
correcter->Update();
}
catch (const itk::ExceptionObject & e)
{
if (verbose)
{
std::cerr << "Exception caught: " << e << std::endl;
}
return EXIT_FAILURE;
}
if (verbose)
{
correcter->Print(std::cout, 3);
}
timer.Stop();
if (verbose)
{
std::cout << "Elapsed time: " << timer.GetMean() << std::endl;
}
/**
* output
*/
typename itk::ants::CommandLineParser::OptionType::Pointer outputOption = parser->GetOption("output");
if (outputOption && outputOption->GetNumberOfFunctions())
{
/**
* Reconstruct the bias field at full image resolution. Divide
* the original input image by the bias field to get the final
* corrected image.
*/
using BSplinerType = itk::BSplineControlPointImageFilter<typename CorrecterType::BiasFieldControlPointLatticeType,
typename CorrecterType::ScalarImageType>;
typename BSplinerType::Pointer bspliner = BSplinerType::New();
bspliner->SetInput(correcter->GetLogBiasFieldControlPointLattice());
bspliner->SetSplineOrder(correcter->GetSplineOrder());
bspliner->SetSize(inputImage->GetLargestPossibleRegion().GetSize());
bspliner->SetOrigin(newOrigin);
bspliner->SetDirection(inputImage->GetDirection());
bspliner->SetSpacing(inputImage->GetSpacing());
bspliner->Update();
typename ImageType::Pointer logField = AllocImage<ImageType>(inputImage);
itk::ImageRegionIterator<typename CorrecterType::ScalarImageType> ItB(
bspliner->GetOutput(), bspliner->GetOutput()->GetLargestPossibleRegion());
itk::ImageRegionIterator<ImageType> ItF(logField, logField->GetLargestPossibleRegion());
for (ItB.GoToBegin(), ItF.GoToBegin(); !ItB.IsAtEnd(); ++ItB, ++ItF)
{
ItF.Set(ItB.Get()[0]);
}
using ExpFilterType = itk::ExpImageFilter<ImageType, ImageType>;
typename ExpFilterType::Pointer expFilter = ExpFilterType::New();
expFilter->SetInput(logField);
expFilter->Update();
using DividerType = itk::DivideImageFilter<ImageType, ImageType, ImageType>;
typename DividerType::Pointer divider = DividerType::New();
divider->SetInput1(inputImage);
divider->SetInput2(expFilter->GetOutput());
typename ImageType::Pointer dividedImage = divider->GetOutput();
dividedImage->Update();
dividedImage->DisconnectPipeline();
if (maskImageOption && maskImageOption->GetNumberOfFunctions() > 0)
{
itk::ImageRegionIteratorWithIndex<ImageType> ItD(dividedImage, dividedImage->GetLargestPossibleRegion());
itk::ImageRegionIterator<ImageType> ItI(inputImage, inputImage->GetLargestPossibleRegion());
for (ItD.GoToBegin(), ItI.GoToBegin(); !ItD.IsAtEnd(); ++ItD, ++ItI)
{
if (itk::Math::FloatAlmostEqual(maskImage->GetPixel(ItD.GetIndex()),
itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue()))
{
ItD.Set(ItI.Get());
}
}
}
bool doRescale = true;
typename itk::ants::CommandLineParser::OptionType::Pointer rescaleOption = parser->GetOption("rescale-intensities");
if (!isMaskImageSpecified || (rescaleOption && rescaleOption->GetNumberOfFunctions() &&
!parser->Convert<bool>(rescaleOption->GetFunction()->GetName())))
{
doRescale = false;
}
if (doRescale)
{
typename ThresholderType::Pointer thresholder2 = ThresholderType::New();
thresholder2->SetInsideValue(itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue());
thresholder2->SetOutsideValue(itk::NumericTraits<typename MaskImageType::PixelType>::OneValue());
thresholder2->SetLowerThreshold(itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue());
thresholder2->SetUpperThreshold(itk::NumericTraits<typename MaskImageType::PixelType>::ZeroValue());
thresholder2->SetInput(maskImage);
typename StatsType::Pointer statsBiasCorrected = StatsType::New();
statsBiasCorrected->SetInput(dividedImage);
statsBiasCorrected->SetLabelInput(thresholder2->GetOutput());
statsBiasCorrected->UseHistogramsOff();
statsBiasCorrected->Update();
RealType minBiasCorrected = statsBiasCorrected->GetMinimum(maskLabel);
RealType maxBiasCorrected = statsBiasCorrected->GetMaximum(maskLabel);
RealType slope = (maxOriginal - minOriginal) / (maxBiasCorrected - minBiasCorrected);
itk::ImageRegionIteratorWithIndex<ImageType> ItD(dividedImage, dividedImage->GetLargestPossibleRegion());
for (ItD.GoToBegin(); !ItD.IsAtEnd(); ++ItD)
{
if (itk::Math::FloatAlmostEqual(maskImage->GetPixel(ItD.GetIndex()), static_cast<RealType>(maskLabel)))
{
RealType originalIntensity = ItD.Get();
RealType rescaledIntensity = maxOriginal - slope * (maxBiasCorrected - originalIntensity);
ItD.Set(rescaledIntensity);
}
}
}
typename ImageType::RegionType inputRegion;
inputRegion.SetIndex(inputImageIndex);
inputRegion.SetSize(inputImageSize);
using CropperType = itk::ExtractImageFilter<ImageType, ImageType>;
typename CropperType::Pointer cropper = CropperType::New();
cropper->SetInput(dividedImage);
cropper->SetExtractionRegion(inputRegion);
cropper->SetDirectionCollapseToSubmatrix();
cropper->Update();
typename CropperType::Pointer biasFieldCropper = CropperType::New();
biasFieldCropper->SetInput(expFilter->GetOutput());
biasFieldCropper->SetExtractionRegion(inputRegion);
biasFieldCropper->SetDirectionCollapseToSubmatrix();
biasFieldCropper->Update();
if (outputOption->GetFunction(0)->GetNumberOfParameters() == 0)
{
ANTs::WriteImage<ImageType>(cropper->GetOutput(), (outputOption->GetFunction(0)->GetName()).c_str());
}
else if (outputOption->GetFunction(0)->GetNumberOfParameters() > 0)
{
ANTs::WriteImage<ImageType>(cropper->GetOutput(), (outputOption->GetFunction(0)->GetParameter(0)).c_str());
if (outputOption->GetFunction(0)->GetNumberOfParameters() > 1)
{
ANTs::WriteImage<ImageType>(biasFieldCropper->GetOutput(),
(outputOption->GetFunction(0)->GetParameter(1)).c_str());
}
}
}
return EXIT_SUCCESS;
}
void
N3InitializeCommandLineOptions(itk::ants::CommandLineParser * parser)
{
using OptionType = itk::ants::CommandLineParser::OptionType;
{
std::string description = std::string("This option forces the image to be treated as a specified-") +
std::string("dimensional image. If not specified, N3 tries to ") +
std::string("infer the dimensionality from the input image.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("image-dimensionality");
option->SetShortName('d');
option->SetUsageOption(0, "2/3/4");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("A scalar image is expected as input for bias correction. ") +
std::string("Since N3 log transforms the intensities, negative values ") +
std::string("or values close to zero should be processed prior to ") +
std::string("correction.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("input-image");
option->SetShortName('i');
option->SetUsageOption(0, "inputImageFilename");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description =
std::string("If a mask image is specified, the final bias correction is ") +
std::string("only performed in the mask region. If a weight image is not ") +
std::string("specified, only intensity values inside the masked region are ") +
std::string("used during the execution of the algorithm. If a weight ") +
std::string("image is specified, only the non-zero weights are used in the ") +
std::string("execution of the algorithm although the mask region defines ") +
std::string("where bias correction is performed in the final output. ") +
std::string("Otherwise bias correction occurs over the entire image domain. ") +
std::string("See also the option description for the weight image. ") +
std::string("If a mask image is *not* specified then the entire image region ") +
std::string("will be used as the mask region. Note that this is different than ") +
std::string("the N3 implementation which uses the results of Otsu thresholding ") +
std::string("to define a mask. However, this leads to unknown anatomical regions being ") +
std::string("included and excluded during the bias correction.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("mask-image");
option->SetShortName('x');
option->SetUsageOption(0, "maskImageFilename");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("At each iteration, a new intensity mapping is calculated ") +
std::string("and applied but there is nothing which constrains the ") +
std::string("new intensity range to be within certain values. The ") +
std::string("result is that the range can \"drift\" from the original ") +
std::string("at each iteration. This option rescales to the [min,max] ") +
std::string("range of the original image intensities within the user-specified mask.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("rescale-intensities");
option->SetShortName('r');
option->SetUsageOption(0, "0/(1)");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("The weight image allows the user to perform a relative ") +
std::string("weighting of specific voxels during the B-spline fitting. ") +
std::string("For example, some studies have shown that N3 performed on ") +
std::string("white matter segmentations improves performance. If one ") +
std::string("has a spatial probability map of the white matter, one can ") +
std::string("use this map to weight the b-spline fitting towards those ") +
std::string("voxels which are more probabilistically classified as white ") +
std::string("matter. See also the option description for the mask image.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("weight-image");
option->SetUsageOption(0, "weightImageFilename");
option->SetShortName('w');
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("Running N3 on large images can be time consuming. ") +
std::string("To lessen computation time, the input image can be resampled. ") +
std::string("The shrink factor, specified as a single integer, describes ") +
std::string("this resampling. Shrink factors <= 4 are commonly used.") +
std::string("Note that the shrink factor is only applied to the first two or ") +
std::string("three dimensions which we assume are spatial. ");
OptionType::Pointer option = OptionType::New();
option->SetLongName("shrink-factor");
option->SetShortName('s');
option->SetUsageOption(0, "1/2/3/(4)/...");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("Convergence is determined by calculating the sigma of the coefficient of ") +
std::string("variation between subsequent iterations. When this value ") +
std::string("is less than the specified threshold ") +
std::string("from the previous iteration or the maximum number of ") +
std::string("iterations is exceeded the program terminates.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("convergence");
option->SetShortName('c');
option->SetUsageOption(0, "[<numberOfIterations=50>,<convergenceThreshold=0.0>]");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description =
std::string("These options describe the b-spline fitting parameters. ") + std::string("The b-spline mesh is ") +
std::string("specified either as the number of elements in each dimension, ") +
std::string("e.g. 2x2x3 for 3-D images, or it can be specified as a ") +
std::string("single scalar parameter which describes the isotropic sizing ") +
std::string("of the mesh elements. The latter option is typically preferred. ") +
std::string("The default setting ") +
std::string("is to employ a single mesh element over the entire domain, i.e., ") + std::string("-b [1x1x1,4,3].");
OptionType::Pointer option = OptionType::New();
option->SetLongName("bspline-fitting");
option->SetShortName('b');
option->SetUsageOption(0, "[splineDistance,<numberOfFittingLevels=4>,<splineOrder=3>]");
option->SetUsageOption(1, "[meshResolution,<numberOfFittingLevels=4>,<splineOrder=3>]");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("These options describe the histogram sharpening parameters, ") +
std::string("i.e. the deconvolution step parameters described in the ") +
std::string("original N3 algorithm. The default values have been shown ") +
std::string("to work fairly well.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("histogram-sharpening");
option->SetShortName('t');
option->SetUsageOption(0, "[<FWHM=0.15>,<wienerNoise=0.01>,<numberOfHistogramBins=200>]");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("The output consists of the bias corrected version of the ") +
std::string("input image. Optionally, one can also output the estimated ") +
std::string("bias field.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("output");
option->SetShortName('o');
option->SetUsageOption(0, "correctedImage");
option->SetUsageOption(1, "[correctedImage,<biasField>]");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("Get Version Information.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("version");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("Verbose output.");
OptionType::Pointer option = OptionType::New();
option->SetShortName('v');
option->SetLongName("verbose");
option->SetUsageOption(0, "(0)/1");
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("Print the help menu (short version).");
OptionType::Pointer option = OptionType::New();
option->SetShortName('h');
option->SetDescription(description);
parser->AddOption(option);
}
{
std::string description = std::string("Print the help menu.");
OptionType::Pointer option = OptionType::New();
option->SetLongName("help");
option->SetDescription(description);
parser->AddOption(option);
}
}
// entry point for the library; parameter 'args' is equivalent to 'argv' in (argc,argv) of commandline parameters to
// 'main()'
int
N3BiasFieldCorrection(std::vector<std::string> args, std::ostream * /*out_stream = nullptr */)
{
if (args.size() >= 1 && std::string(args[0]) != std::string("2") && std::string(args[0]) != std::string("3") &&
std::string(args[0]) != std::string("4"))
{
// put the arguments coming in as 'args' into standard (argc,argv) format;
// 'args' doesn't have the command name as first, argument, so add it manually;
// 'args' may have adjacent arguments concatenated into one argument,
// which the parser should handle
args.insert(args.begin(), "N3BiasFieldCorrection");
int argc = args.size();
char ** argv = new char *[args.size() + 1];
for (unsigned int i = 0; i < args.size(); ++i)
{
// allocate space for the string plus a null character
argv[i] = new char[args[i].length() + 1];
std::strncpy(argv[i], args[i].c_str(), args[i].length());
// place the null character in the end
argv[i][args[i].length()] = '\0';
}
argv[argc] = nullptr;
// class to automatically cleanup argv upon destruction
class Cleanup_argv
{
public:
Cleanup_argv(char ** argv_, int argc_plus_one_)
: argv(argv_)
, argc_plus_one(argc_plus_one_)
{}
~Cleanup_argv()
{
for (unsigned int i = 0; i < argc_plus_one; ++i)
{
delete[] argv[i];
}
delete[] argv;
}
private:
char ** argv;
unsigned int argc_plus_one;
};
Cleanup_argv cleanup_argv(argv, argc + 1);
// antscout->set_stream( out_stream );
itk::ants::CommandLineParser::Pointer parser = itk::ants::CommandLineParser::New();
parser->SetCommand(argv[0]);
std::string commandDescription = std::string("This N3 is a variant of the popular N3 (nonparameteric nonuniform ") +
std::string("normalization) retrospective bias correction algorithm. Based ") +
std::string("on the assumption that the corruption of the low frequency bias ") +
std::string("field can be modeled as a convolution of the intensity histogram ") +
std::string("by a Gaussian, the basic algorithmic protocol is to iterate ") +
std::string("between deconvolving the intensity histogram by a Gaussian, ") +
std::string("remapping the intensities, and then spatially smoothing this ") +
std::string("result by a B-spline modeling of the bias field itself. ");
parser->SetCommandDescription(commandDescription);
N3InitializeCommandLineOptions(parser);
if (parser->Parse(argc, argv) == EXIT_FAILURE)
{
return EXIT_FAILURE;
}
if (argc == 1)
{
parser->PrintMenu(std::cerr, 5, false);
return EXIT_FAILURE;
}
else if (parser->GetOption("help")->GetFunction() &&
parser->Convert<bool>(parser->GetOption("help")->GetFunction()->GetName()))
{
parser->PrintMenu(std::cout, 5, false);
return EXIT_SUCCESS;
}
else if (parser->GetOption('h')->GetFunction() &&