Stochastic Modeling for Medical Image Analysis

299 Pages·2016·166.199 MB·English

by  El-Baz, Ayman S., Gimelʹfarb, Georgiĭ Lʹvovich, Suri, Jasjit S

Stochastic Modeling
Medical Image
for
Analysis

Stochastic Modeling
Medical Image
for
Analysis
Ayman El-Baz
Georgy Gimel’farb
Jasjit S. Suri
Boca Raton London New York
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Contents
Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .ix
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xiii
Abbreviations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
1. MedicalImagingModalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 MagneticResonanceImaging . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 StructuralMRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.2 DynamicContrast-EnhancedMRI . . . . . . . . . . . . . . . . . 4
1.1.3 DiffusionMRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.4 FunctionalMRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.1.5 MagneticResonanceAngiography . . . . . . . . . . . . . . . . 7
1.1.6 TaggedMRI,MRS,andPWI . . . . . . . . . . . . . . . . . . . . . 8
1.1.7 MRI:ProsandCons . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2 ComputedTomography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.1 StructuralCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10
1.2.2 Contrast–EnhancedCT . . . . . . . . . . . . . . . . . . . . . . . . .10
1.2.3 CTAngiography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
1.2.4 Microtomography. . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
1.2.5 CTImaging:ProsandCons. . . . . . . . . . . . . . . . . . . . . .12
1.3 UltrasoundImaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13
1.4 NuclearMedicalImaging(NuclideImaging) . . . . . . . . . . . . . .17
1.5 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . . .21
2. FromImagestoGraphicalModels . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1 BasicsofImageModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . .26
2.1.1 DigitalImages,Videos,andRegionMaps . . . . . . . . . . .26
2.1.2 ImageHomogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . .28
2.1.3 ProbabilityModelsofImagesandRegionMaps . . . . . .29
2.1.4 OptimalStatisticalInference . . . . . . . . . . . . . . . . . . . . .31
2.1.5 UnessentialImageDeviations . . . . . . . . . . . . . . . . . . . .32
2.2 Pixel/VoxelInteractionsandNeighborhoods . . . . . . . . . . . . . .35
2.2.1 MarkovRandomField(MRF) . . . . . . . . . . . . . . . . . . . .37
2.2.2 BasicStochasticModelingScenarios . . . . . . . . . . . . . . .40
2.2.3 InvariancetoUnessentialDeviations. . . . . . . . . . . . . . .41
2.2.3.1 MultipleSecond-andHigher-Order
Interactions. . . . . . . . . . . . . . . . . . . . . . . . . . .42
2.2.3.2 Contrast/Offset-InvariantMGRFs . . . . . . . . . .43
2.3 ExponentialFamiliesofProbabilityDistributions . . . . . . . . . . .44
2.3.1 LearninganExponentialFamily . . . . . . . . . . . . . . . . . .48
v
vi Contents
2.4 AppearanceandShapeModeling . . . . . . . . . . . . . . . . . . . . . . .50
2.5 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . . .55
2.5.1 ShapeModelingwithDeformableModels. . . . . . . . . . .58
3. IRFModels:EstimatingMarginals . . . . . . . . . . . . . . . . . . . . . . . 63
3.1 BasicIndependentRandomFields . . . . . . . . . . . . . . . . . . . . . .63
3.2 SupervisedandUnsupervisedLearning . . . . . . . . . . . . . . . . . .65
3.2.1 ParametricVersusNonparametricModels. . . . . . . . . . .68
3.3 Expectation-MaximizationtoIdentifyMixtures . . . . . . . . . . . .68
3.4 GaussianLinearCombinationsVersusMixtures . . . . . . . . . . . .71
3.4.1 SequentialInitializationofanLCG/LCDGModel . . . . .73
3.4.2 RefinementofanLCG/LCDGModel . . . . . . . . . . . . . .75
3.4.3 ModelPartitioningbyAllocatingSubordinateTerms. . .77
3.5 Pseudo-MarginalsinMedicalImageAnalysis. . . . . . . . . . . . . .78
3.5.1 SyntheticCheckerboardImages . . . . . . . . . . . . . . . . . .79
3.5.2 ModelingLungsonSpiralLDCTChestScans . . . . . . . .83
3.5.3 ModelingBloodVesselsonTOF-MRAImages . . . . . . . .86
3.5.4 ModelingBrainTissuesonMRI. . . . . . . . . . . . . . . . . . .89
3.5.5 ModelingBrainBloodVesselsonPC-MRAImages . . . .90
3.5.6 AortaModelingonCTAImages . . . . . . . . . . . . . . . . . .91
3.6 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . . .94
4. Markov-GibbsRandomFieldModels:EstimatingSignal
Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.1 GenericKth-OrderMGRFs . . . . . . . . . . . . . . . . . . . . . . . . . . . .97
4.1.1 MCMCSamplingofanMGRF . . . . . . . . . . . . . . . . . .100
4.1.2 GibbsandMetropolis-HastingsSamplers . . . . . . . . . .101
4.2 CommonSecond-andHigher-OrderMGRFs . . . . . . . . . . . . .104
4.2.1 Nearest-NeighborMGRFs. . . . . . . . . . . . . . . . . . . . . .105
4.2.2 GaussianandGauss-MarkovRandomFields. . . . . . . .111
4.2.3 ModelswithMultiplePairwiseInteractions. . . . . . . . .113
4.2.4 Higher-OrderMGRFs . . . . . . . . . . . . . . . . . . . . . . . . .117
4.3 LearningSecond-OrderInteractionStructures . . . . . . . . . . . .119
4.4 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .123
4.4.1 ImageFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126
4.4.2 ImageSampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . .127
4.4.3 ModelLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . .127
5. Applications:ImageAlignment. . . . . . . . . . . . . . . . . . . . . . . . . . 129
5.1 GeneralImageAlignmentFrameworks . . . . . . . . . . . . . . . . .129
5.2 GlobalAlignmentbyLearninganAppearancePrior. . . . . . . .131
5.3 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .134
6. SegmentingMultimodalImages . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.1 JointMGRFofImagesandRegionMaps. . . . . . . . . . . . . . . . .144
Contents vii
6.2 ExperimentalValidation. . . . . . . . . . . . . . . . . . . . . . . . . . . . .147
6.2.1 SyntheticData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .147
6.2.2 LungLDCTImages . . . . . . . . . . . . . . . . . . . . . . . . . .150
6.2.3 BloodVesselsinTOF-MRAImages . . . . . . . . . . . . . . .154
6.2.4 BloodVesselsinPC-MRAImages . . . . . . . . . . . . . . . .158
6.2.5 AortaBloodVesselsinCTAImages. . . . . . . . . . . . . . .161
6.2.6 BrainMRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .162
6.3 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .167
6.4 PerformanceEvaluationandValidation . . . . . . . . . . . . . . . . .169
7. SegmentingwithDeformableModels . . . . . . . . . . . . . . . . . . . . . 173
7.1 Appearance-BasedSegmentation . . . . . . . . . . . . . . . . . . . . . .173
7.1.1 ExperimentalValidation . . . . . . . . . . . . . . . . . . . . . . .175
7.1.1.1 Starfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175
7.1.1.2 Hand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .176
7.1.1.3 Bones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178
7.1.1.4 VariousOtherObjects . . . . . . . . . . . . . . . . . .178
7.2 ShapeandAppearance-BasedSegmentation. . . . . . . . . . . . . .183
7.2.1 LearningaShapeModel . . . . . . . . . . . . . . . . . . . . . . .185
7.2.2 ExperimentalValidation . . . . . . . . . . . . . . . . . . . . . . .185
7.2.2.1 Starfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . .185
7.2.2.2 Kidney . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189
7.3 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .193
8. SegmentingwithShapeandAppearancePriors . . . . . . . . . . . . . 197
8.1 LearningaShapePrior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .197
8.2 EvolvingaDeformableBoundary. . . . . . . . . . . . . . . . . . . . . .200
8.3 ExperimentalValidation. . . . . . . . . . . . . . . . . . . . . . . . . . . . .201
8.4 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .203
9. CineCardiacMRIAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
9.1 SegmentingMyocardialBorders. . . . . . . . . . . . . . . . . . . . . . .210
9.2 WallThicknessAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .214
9.2.1 GGMRF-BasedContinuityAnalysis . . . . . . . . . . . . . .215
9.3 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .217
9.3.1 LVWallCorrespondences . . . . . . . . . . . . . . . . . . . . . .218
9.3.2 LVWallSegmentation. . . . . . . . . . . . . . . . . . . . . . . . .219
9.3.3 WallThickening . . . . . . . . . . . . . . . . . . . . . . . . . . . . .225
9.4 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .227
10. SizingCardiacPathologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
10.1 LVWallSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .236
10.2 IdentifyingthePathologicalTissue . . . . . . . . . . . . . . . . . . . . .241
10.3 QuantifyingtheMyocardialViability . . . . . . . . . . . . . . . . . . .242
10.4 PerformanceEvaluationandValidation . . . . . . . . . . . . . . . . .243
viii Contents
10.4.1 SegmentationAccuracy . . . . . . . . . . . . . . . . . . . . . . .244
10.4.2 TransmuralExtentAccuracy . . . . . . . . . . . . . . . . . . . .244
10.4.3 PathologyDelineationAccuracy . . . . . . . . . . . . . . . . .247
10.4.4 ClinicallyMeaningfulEffects . . . . . . . . . . . . . . . . . . .249
10.5 BibliographicandHistoricalNotes . . . . . . . . . . . . . . . . . . . . .253
10.5.1 AppearanceandShapePriors . . . . . . . . . . . . . . . . . . .253
10.5.2 MyocardialViabilityMetrics . . . . . . . . . . . . . . . . . . . .254
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277