Biomedical Texture Analysis: Fundamentals, Tools and Challenges

431 Pages·2017·19.878 MB·English

by  Adrien Depeursinge (editor), Omar S. Al-Kadi (editor), J. Ross Mitchell (editor)

BIOMEDICAL
TEXTURE ANALYSIS
FUNDAMENTALS, TOOLS
AND CHALLENGES
Edited by
Adrien Depeursinge,
Omar S Al-Kadi, and J. Ross Mitchell
BIOMEDICAL
TEXTURE ANALYSIS
The Elsevier and MICCAI Society
Book Series
Advisory board
StephenAylward(Kitware,USA)
DavidHawkes(UniversityCollegeLondon,UnitedKingdom)
KensakuMori(UniversityofNagoya,Japan)
AlisonNoble(UniversityofOxford,UnitedKingdom)
SoniaPujol(HarvardUniversity,USA)
DanielRueckert(ImperialCollege,UnitedKingdom)
XavierPennec(INRIASophia-Antipolis,France)
PierreJannin(UniversityofRennes,France)
Alsoavailable:
Balocco,ComputingandVisualizationforIntravascularImagingand
ComputerAssistedStenting,9780128110188
Wu,MachineLearningandMedicalImaging,9780128040768
Zhou,MedicalImageRecognition,SegmentationandParsing,
9780128025819
BIOMEDICAL
TEXTURE ANALYSIS
Fundamentals, Tools
and Challenges
Editedby
ADRIENDEPEURSINGE
University of Applied Sciences Western Switzerland
(HES-SO)
OMARS.AL-KADI
King Abdullah II School for Information Technology,
University of Jordan
J.ROSSMITCHELL
Mayo Clinic College of Medicine,
and Department of Biomedical Informatics
at Arizona State University, USA
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CONTENTS
Preface xiii
1. FundamentalsofTextureProcessingforBiomedicalImageAnalysis 1
AdrienDepeursinge,JulienFageot,OmarS.Al-Kadi
1.1. Introduction 1
1.2. Biomedicaltextureprocesses 3
1.2.1. Imageintensityversusimagetexture 3
1.2.2. Notationandsampling 5
1.2.3. Texturefunctionsasrealizationsoftextureprocesses 7
1.2.4. Primitivesandtextons 9
1.2.5. Biomedicalimagemodalities 12
1.3. BiomedicalTextureAnalysis(BTA) 12
1.3.1. Textureoperatorsandaggregationfunctions 12
1.3.2. Normalization 18
1.3.3. Invariances 19
1.4. Conclusions 23
Acknowledgements 24
References 24
2. MultiscaleandMultidirectionalBiomedicalTextureAnalysis 29
AdrienDepeursinge
2.1. Introduction 29
2.2. Notation 31
2.3. Multiscaleimageanalysis 31
2.3.1. Spatialversusspectralcoverageoflinearoperators:theuncertaintyprinciple 32
2.3.2. Regionofinterestandresponsemapaggregation 34
2.4. Multidirectionalimageanalysis 38
2.4.1. TheLocalOrganizationofImageDirections(LOID) 40
2.4.2. Directionalsensitivityoftextureoperators 41
2.4.3. Locallyrotation-invariantoperatorsandmovingframesrepresentations 44
2.4.4. Directionallyinsensitive,sensitive,andmovingframesrepresentationsfortexture
classification:aquantitativeperformancecomparison 46
2.5. Discussionsandconclusions 48
Acknowledgements 50
References 50
3. BiomedicalTextureOperatorsandAggregationFunctions 55
AdrienDepeursinge,JulienFageot
3.1. Introduction 55
v
vi Contents
3.2. Convolutionalapproaches 57
3.2.1. Circularly/sphericallysymmetricfilters 59
3.2.2. Directionalfilters 59
3.2.3. Learnedfilters 71
3.3. Gray-levelmatrices 79
3.3.1. Gray-LevelCooccurrenceMatrices(GLCM) 80
3.3.2. Gray-LevelRun-LengthMatrices(GLRLM) 81
3.3.3. Gray-LevelSizeZoneMatrices(GLSZM) 82
3.4. LocalBinaryPatterns(LBP) 84
3.5. Fractals 87
3.6. Discussionsandconclusions 89
Acknowledgements 91
References 91
4. DeepLearninginTextureAnalysisandItsApplicationtoTissueImage
Classification 95
VincentAndrearczyk,PaulF.Whelan
4.1. Introduction 95
4.2. Introductiontoconvolutionalneuralnetworks 96
4.2.1. Neuronsandnonlinearity 96
4.2.2. Neuralnetwork 97
4.2.3. Training 98
4.2.4. CNN 101
4.3. Deeplearningfortextureanalysis:literaturereview 105
4.3.1. Earlywork 105
4.3.2. TexturespecificCNNs 106
4.3.3. CNNsforbiomedicaltextureclassification 107
4.4. End-to-endtextureCNN:proposedsolution 108
4.4.1. Method 109
4.4.2. Experiments 111
4.4.3. Resultsanddiscussions 113
4.4.4. Combiningtextureandshapeanalysis 118
4.5. Applicationtotissueimagesclassification 119
4.5.1. State-of-the-art 120
4.5.2. Method 120
4.5.3. Datasets 121
4.5.4. Resultsanddiscussions 124
4.6. Conclusion 126
References 126
5. FractalsforBiomedicalTextureAnalysis 131
OmarS.Al-Kadi
5.1. Introduction 131
5.2. Tissuetexture 133
Contents vii
5.3. Basicconceptsoffractalgeometry 136
5.3.1. Self-similarity 136
5.3.2. Orderedhierarchy 137
5.3.3. Degreeofirregularity 137
5.4. Methodsforcomputingfractaldimensions 140
5.4.1. DifferentialBox-Counting(DBC)method 141
5.4.2. FractionalBrownianmotionmethods 142
5.4.3. Area-basedmethods 143
5.5. Typesoffractals 146
5.6. FDparameterestimationoptimization 147
5.7. Lacunarityanalysis 148
5.7.1. Assessingimagetexturesparsity 148
5.7.2. Rotation-invariance 150
5.7.3. Clinicalsignificance 150
5.8. Tumortissuecharacterization 152
5.8.1. Surfaceroughness 153
5.8.2. Tumorfractalanalysis 154
5.9. Considerationsforfractalanalysis 157
5.9.1. ChoosingasuitablemethodforestimatingtheFD 157
5.9.2. Multivariatefractalanalysis 157
5.9.3. Performingfractalormultifractalanalysis 157
5.10. Conclusion 157
References 158
6. HandlingofFeatureSpaceComplexityforTextureAnalysisinMedicalImages 163
YangSong,WeidongCai
6.1. Introduction 163
6.2. Applicationsoftextureanalysis 164
6.2.1. Lesiondetection 164
6.2.2. Diseasecategorization 166
6.2.3. Imageretrieval 167
6.3. Reviewofclassificationmethods 168
6.3.1. Ensembleclassification 169
6.3.2. Subcategorization 170
6.3.3. Sparserepresentation 170
6.4. Subcategory-basedensembleclassification 171
6.4.1. LargeMarginLocalEstimate(LMLE) 172
6.4.2. Locally-constrainedsubclusterrepresentationensemble 176
6.5. Experiments 179
6.5.1. Datasetandimplementation 179
6.5.2. Resultsofpatchclassification 181
6.6. Conclusions 186
References 186
viii Contents
7. RigidMotionInvariantClassificationof3DTexturesandItsApplicationto
HepaticTumorDetection 193
SanatUpadhyay,SaurabhJain,ManosPapadakis
7.1. Introduction 193
7.2. Isotropicmultiresolutionanalysis 198
7.3. Implementingrotationswithcompactlysupportedrefinablefunctions 204
7.4. ConnectingIMRAwithstochastictexturemodelsusingGaussianMarkovRandom
Fields(GMRF) 206
7.5. Featurespaceforrotationallyinvarianttexturediscrimination 211
7.5.1. Self-distancefor3Dtextures 212
7.6. 3Dtexture-basedfeatures 212
7.7. Conclusion 215
References 218
8. AnIntroductiontoRadiomics:AnEvolvingCornerstoneofPrecisionMedicine 223
SaraRanjbar,J.RossMitchell
8.1. Introduction 223
8.2. Backgroundoncancercare 224
8.2.1. Biomarkersandcancercare 226
8.2.2. Limitationsofresponseassessmentprocess 227
8.2.3. Limitationsofcharacterizationprocess 228
8.2.4. Limitationsofcurrentbiomarkers 229
8.3. Thepotentialareasofradiomicsutility 229
8.4. Workflowofradiomics 231
8.4.1. Imageacquisition 232
8.4.2. Segmentation 233
8.4.3. Featureextraction 234
8.4.4. Analysisandvalidation 235
8.5. Examplesofradiomicsliterature 236
8.6. Challengesofradiomics 238
8.7. Conclusions 239
References 239
9. DeepLearningTechniquesonTextureAnalysisofChestandBreastImages 247
Jie-ZhiCheng,Chung-MingChen,DinggangShen
9.1. Introduction 247
9.2. Computer-aideddetection 248
9.2.1. LungnoduledetectioninCTscans 249
9.2.2. OtherdetectionproblemsforCTscans 252
9.3. Computer-aideddiagnosis 253
9.3.1. Computer-aideddiagnosisonbreastlesionsinultrasoundimages 255
9.3.2. Computer-aideddiagnosisonpulmonarynodulesinCTscans 259
9.3.3. Othercomputer-aideddiagnosisproblemsinpulmonaryCTscans 264
Contents ix
9.4. Automaticmappingfromimagecontenttothesemanticterms 265
9.4.1. Semanticmappingwiththeconventionalpatternrecognitionparadigm 266
9.4.2. Deeplearningforsemanticmapping 268
9.5. Conclusion 273
Acknowledgements 273
References 273
10.AnalysisofHistopathologyImages 281
OscarJimenez-del-Toro,SebastianOtálora,MatsAndersson,KristianEurén,
MartinHedlund,MikaelRousson,HenningMüller,ManfredoAtzori
10.1. Histopathologyimaging:achallengefortextureanalysis 281
10.2. Traditionalmachinelearningapproaches 284
10.2.1. Preprocessing 284
10.2.2. Detectionandsegmentationofstructures 285
10.2.3. Featureextraction 286
10.2.4. Featureselectionanddimensionalityreduction 287
10.2.5. Classification 288
10.3. Deeplearningapproaches 289
10.3.1. Supervisedandunsupervisedfeaturelearningarchitectures 290
10.3.2. Deepconvolutionalneuralnetworks 292
10.3.3. Deeplearningapproachestohistopathologyimageanalysis 292
10.4. Histopathologychallenges 295
10.4.1. Datasets 295
10.4.2. Tasks 296
10.4.3. Evaluationmetrics 297
10.5. Detectingmitoses 299
10.6. Frameandwholeslideimageclassification 302
10.7. Structuresegmentation 303
10.8. Discussionandconclusions 306
Acknowledgements 308
References 308
11.MaZda–AFrameworkforBiomedicalImageTextureAnalysisandData
Exploration 315
PiotrM.Szczypin´ski,ArturKlepaczko
11.1. Introduction 315
11.1.1. Relatedwork 317
11.2. TextureanalysiswithMaZda 318
11.2.1. Overviewoftheimageanalysisworkflows 318
11.2.2. Regionsofinterest 321
11.2.3. Featureextraction 322
11.2.4. Imagepreprocessing 326
11.2.5. Featurenamingconvention 327
11.2.6. Featuremapsandimagesegmentation 328