Biomedical Image Understanding: Methods and Applications

512 Pages·2015·6.78 MB·English

by  Joo-Hwee Lim, Sim-Heng Ong, Wei Xiong

BIOMEDICAL IMAGE
UNDERSTANDING
WILEY SERIES IN BIOMEDICAL ENGINEERING AND
MULTIDISCIPLINARY INTEGRATED SYSTEMS
KAI CHANG, SERIES EDITOR
Advances in Optical Imaging for Clinical Medicine
Nicusor Iftimia, William R. Brugge, and Daniel X. Hammer (Editors)
Antigen Retrieval Immunohistochemistry Based Research and Diagnostics
Shan-Rong Shi and Clive R. Taylor
Introduction to Nanomedicine and Nanobioengineering
Paras N. Prasad
Biomedical Image Understanding
Joo-Hwee Lim, Sim-Heng Ong, and Wei Xiong (Editors)
BIOMEDICAL IMAGE
UNDERSTANDING
Methods and Applications
JOO-HWEELIM
InstituteforInfocommResearch
SIM-HENGONG
NationalUniversityofSingapore
WEIXIONG
InstituteforInfocommResearch
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LibraryofCongressCataloging-in-PublicationData:
Biomedicalimageunderstanding:methodsandapplications/[editedby]JooHweeLim,SimHengOng,
WeiXiong.
p.;cm.–(WileySeriesinbiomedicalengineeringandmultidisciplinaryintegratedsystems)
Includesbibliographicalreferencesandindex.
ISBN978-1-118-71515-4(cloth)
I.Lim,JooHwee,1964-editor.II.Ong,SimHeng,1955-editor.III.Xiong,Wei,1966-editor.
[DNLM:1. ImageInterpretation,Computer-Assisted.2. ImageEnhancement–methods.3. Image
Processing,Computer-Assisted.4. PatternRecognition,Automated–methods.WB141]
R857.O6
610.28′4–dc23
2014016560
CoverImage:CourtesyoftheAuthor
PrintedintheUnitedStatesofAmerica
10987654321
CONTENTS
ListofContributors xv
Preface xix
Acronyms xxiii
PARTI INTRODUCTION 1
1 OverviewofBiomedicalImageUnderstandingMethods 3
WeiXiong,JierongCheng,YingGu,ShimiaoLiandJooHweeLim
1.1 SegmentationandObjectDetection, 5
1.1.1 MethodsBasedonImageProcessingTechniques, 6
1.1.2 MethodsUsingPatternRecognitionandMachineLearning
Algorithms, 7
1.1.3 ModelandAtlas-BasedSegmentation, 8
1.1.4 MultispectralSegmentation, 9
1.1.5 UserInteractionsinInteractiveSegmentationMethods, 10
1.1.6 FrontiersofBiomedicalImageSegmentation, 11
1.2 Registration, 11
1.2.1 TaxonomyofRegistrationMethods, 12
1.2.2 FrontiersofRegistrationforBiomedicalImageUnderstanding, 15
1.3 ObjectTracking, 16
1.3.1 ObjectRepresentation, 17
1.3.2 FeatureSelectionforTracking, 18
vi CONTENTS
1.3.3 ObjectTrackingTechnique, 19
1.3.4 FrontiersofObjectTracking, 19
1.4 Classification, 20
1.4.1 FeatureExtractionandFeatureSelection, 21
1.4.2 Classifiers, 22
1.4.3 UnsupervisedClassification, 23
1.4.4 ClassifierCombination, 24
1.4.5 FrontiersofPatternClassificationforBiomedicalImage
Understanding, 25
1.5 Knowledge-BasedSystems, 26
1.5.1 SemanticInterpretationandKnowledge-BasedSystems, 26
1.5.2 Knowledge-BasedVisionSystems, 27
1.5.3 Knowledge-BasedVisionSystemsinBiomedicalImage
Analysis, 28
1.5.4 FrontiersofKnowledge-BasedSystems, 29
References, 29
PARTII SEGMENTATIONANDOBJECTDETECTION 47
2 MedicalImageSegmentationanditsApplicationinCardiacMRI 49
DongWei,ChaoLi,andYingSun
2.1 Introduction, 50
2.2 Background, 51
2.2.1 ActiveContourModels, 51
2.2.2 ParametricandNonparametricContourRepresentation, 52
2.2.3 Graph-BasedImageSegmentation, 53
2.2.4 Summary, 54
2.3 ParametricActiveContours–TheSnakes, 54
2.3.1 TheInternalSplineEnergyE , 54
int
2.3.2 TheImage-DerivedEnergyE , 55
img
2.3.3 TheExternalControlEnergyE , 55
con
2.3.4 ExtensionofSnakesandSummaryofParametricActive
Contours, 57
2.4 GeometricActiveContours–TheLevelSets, 58
2.4.1 VariationalLevelSetMethods, 58
2.4.2 Region-BasedVariationalLevelSetMethods, 60
2.4.3 SummaryofLevelSetMethods, 64
2.5 Graph-BasedMethods–TheGraphCuts, 65
2.5.1 BasicGraphCutsFormulation, 65
2.5.2 Patch-BasedGraphCuts, 66
2.5.3 AnExampleofGraphCuts, 68
2.5.4 SummaryofGraphCutMethods, 72
CONTENTS vii
2.6 CaseStudy:CardiacImageSegmentationUsingADualLevelSets
Model, 73
2.6.1 Introduction, 73
2.6.2 Method, 74
2.6.3 ExperimentalResults, 79
2.6.4 ConclusionoftheCaseStudy, 81
2.7 ConclusionandNear-FutureTrends, 81
References, 83
3 MorphometricMeasurementsoftheRetinalVasculatureinFundus
ImagesWithVampire 91
EmanueleTrucco,AndreaGiachetti,LuciaBallerini,DevanjaliRelan,
AlessandroCavinato,andTomMacgillivray
3.1 Introduction, 92
3.2 AssessingVesselWidth, 93
3.2.1 PreviousWork, 93
3.2.2 OurMethod, 94
3.2.3 Results, 95
3.2.4 Discussion, 96
3.3 ArteryorVein? 98
3.3.1 PreviousWork, 98
3.3.2 OurSolution, 99
3.3.3 Results, 101
3.3.4 Discussion, 103
3.4 AreMyProgram’sMeasurementsAccurate? 104
3.4.1 Discussion, 106
References, 107
4 AnalyzingCellandTissueMorphologiesUsingPattern
RecognitionAlgorithms 113
HweeKuanLee,YanNeiLaw,Chao-HuiHuang,andChoonKongYap
4.1 Introduction, 113
4.2 TextureSegmentationofEndometrialImagesUsingtheSubspace
Mumford–ShahModel, 115
4.2.1 SubspaceMumford–ShahSegmentationModel, 116
4.2.2 FeatureWeights, 118
4.2.3 Once-and-For-AllApproach, 119
4.2.4 Results, 119
4.3 SpotClusteringforDetectionofMutantsinKeratinocytes, 120
4.3.1 ImageAnalysisFramework, 123
4.3.2 Results, 124
4.4 CellsandNucleiDetection, 124
4.4.1 Model, 125
4.4.2 NeuralCellsandBreastCancerCellsData, 127
viii CONTENTS
4.4.3 PerformanceEvaluation, 127
4.4.4 RobustnessStudy, 127
4.4.5 Results, 128
4.5 GeometricRegionalGraphSpectralFeature, 134
4.5.1 ConversionofImagePatchesintoRegionSignatures, 134
4.5.2 ComparingRegionSignatures, 135
4.5.3 ClassificationofRegionSignatures, 136
4.5.4 RandomMaskingandObjectDetection, 136
4.5.5 Results, 137
4.6 MitoticCellsintheH&EHistopathologicalImagesofBreastCancer
Carcinoma, 138
4.6.1 MitoticIndexEstimation, 139
4.6.2 MitoticCandidateSelection, 140
4.6.3 ExclusiveIndependentComponentAnalysis(XICA), 140
4.6.4 ClassificationUsingSparseRepresentation, 143
4.6.5 TrainingandTestingOverChannels, 144
4.6.6 Results, 146
4.7 Conclusions, 147
References, 147
PARTIII REGISTRATIONANDMATCHING 153
5 3DNonrigidImageRegistrationbyParzen-Window-BasedNormalized
MutualInformationanditsApplicationonMr-GuidedMicrowave
ThermocoagulationofLiverTumors 155
RuiXu,Yen-WeiChen,ShigehiroMorikawa,andYoshimasaKurumi
5.1 Introduction, 155
5.2 Parzen-Window-BasedNormalizedMutualInformation, 157
5.2.1 DefinitionofParzen-WindowMethod, 157
5.2.2 Parzen-Window-BasedEstimationofJointHistogram, 158
5.2.3 NormalizedMutualInformationanditsDerivative, 160
5.3 AnalysisofKernelSelection, 163
5.3.1 TheDesignedKernel, 163
5.3.2 ComparisoninTheory, 167
5.3.3 ComparisonbyExperiments, 170
5.4 ApplicationonMR-GuidedMicrowaveThermocoagulationofLiver
Tumors, 174
5.4.1 IntroductionofMR-GuidedMicrowaveThermocoagulation
ofLiverTumors, 174
5.4.2 NonrigidRegistrationbyParzen-Window-BasedMutual
Information, 175
5.4.3 EvaluationonPhantomData, 177
5.4.4 EvaluationonClinicalCases, 180