Table Of Content

` UNIVERSITA DEGLI STUDI DI PARMA DIPARTIMENTODIINGEGNERIADELL’INFORMAZIONE Dottorato di Ricerca in Tecnologie dell’Informazione XXVI Ciclo Pablo Mesejo Santiago AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTING DISSERTAZIONEPRESENTATAPERILCONSEGUIMENTO DELTITOLODIDOTTOREDIRICERCA Gennaio2014 ` UNIVERSITA DEGLI STUDI DI PARMA DOTTORATODIRICERCAINTECNOLOGIEDELL’INFORMAZIONE XXVICiclo AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTING Coordinatore: Chiar.mo. Prof. MarcoLocatelli Relatore: Chiar.mo. Prof. StefanoCagnoni Autore: PabloMesejoSantiago Gennaio2014 DedicatedtomymotherandSofia, withgratitude,respectandadmiration. Contents Abstract 9 1 Introduction 10 PartI:Fundamentals 16 2 TheoreticalBackground 17 2.1 MedicalImageSegmentation . . . . . . . . . . . . . . . . . . . . . . 17 2.2 DeformableModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 ParametricDeformableModels . . . . . . . . . . . . . . . . . . . . . 21 GeometricDeformableModels . . . . . . . . . . . . . . . . . . . . . 23 2.3 MedicalImageRegistration . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 TextureandGrayLevelCo-OccurrenceMatrix . . . . . . . . . . . . 29 2.5 SoftComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Classificationproblems . . . . . . . . . . . . . . . . . . . . . . . . . 44 3 Datasets 47 3.1 MedicalImaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 iii CONTENTS iv 3.2 MicroscopyImages . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 ComputedTomographyImages . . . . . . . . . . . . . . . . . . . . . 52 3.4 MagneticResonanceImages . . . . . . . . . . . . . . . . . . . . . . 53 4 MedicalImageSegmentationusingDMsandSC 62 4.1 StatisticalShapeModels . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 LevelSetMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 PartII:ProposedMethods 90 5 HippocampusSegmentationusingASMsandRF 93 5.1 HistologicalImagesandHippocampus . . . . . . . . . . . . . . . . . 93 5.2 DE-basedhippocampuslocalization . . . . . . . . . . . . . . . . . . 98 BestReferenceSliceSelection . . . . . . . . . . . . . . . . . . . . . 98 HippocampusLocalization . . . . . . . . . . . . . . . . . . . . . . . 102 TargetFunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.3 SegmentationusingIterativeOtsu’sThresholdingMethodandRF . . 116 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 ExpansionoftheSegmentation . . . . . . . . . . . . . . . . . . . . . 118 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . 122 5.4 Real-worldapplication . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6 HippocampusSegmentationusingaMH-basedLSApproach 141 6.1 PreviousApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 CONTENTS v 6.2 ProposedMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 TrainingPhase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 TestPhase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 6.3 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . 147 6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 7 BiomedicalISusingGeometricDMsandMHs 154 7.1 ProposedMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Registration-basedprior . . . . . . . . . . . . . . . . . . . . . . . . . 157 Forceterms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Parameterlearningusingmetaheuristics . . . . . . . . . . . . . . . . 160 7.2 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Methodsincludedinthecomparison . . . . . . . . . . . . . . . . . . 164 Parametersettings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Experimentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . 167 7.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 PartIII:FinalRemarks 180 8 FurtherWork 181 9 SummaryandConclusions 183 AppendixI:StatisticaltestsforanalyzingSCtechniquesbehaviour 187 AppendixII:StandardSegmentationMetrics 194 CONTENTS vi AppendixIII:PublicationsJuly2010-July2013 196 AppendixIV:ListofAbbreviations 200 Bibliography 204

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It was in Spain that [my generation] learned that one can be right and yet be beaten, that force can vanquish spirit, that there are times when courage is not its own recompense. Albert Camus. English. First of all, I would like to thank Stefano Cagnoni for his dedication, patience, and constant co
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` UNIVERSITA DEGLI STUDI DI PARMA DIPARTIMENTODIINGEGNERIADELL’INFORMAZIONE Dottorato di Ricerca in Tecnologie dell’Informazione XXVI Ciclo Pablo Mesejo Santiago AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTING DISSERTAZIONEPRESENTATAPERILCONSEGUIMENTO DELTITOLODIDOTTOREDIRICERCA Gennaio2014 ` UNIVERSITA DEGLI STUDI DI PARMA DOTTORATODIRICERCAINTECNOLOGIEDELL’INFORMAZIONE XXVICiclo AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTING Coordinatore: Chiar.mo. Prof. MarcoLocatelli Relatore: Chiar.mo. Prof. StefanoCagnoni Autore: PabloMesejoSantiago Gennaio2014 DedicatedtomymotherandSofia, withgratitude,respectandadmiration. Contents Abstract 9 1 Introduction 10 PartI:Fundamentals 16 2 TheoreticalBackground 17 2.1 MedicalImageSegmentation . . . . . . . . . . . . . . . . . . . . . . 17 2.2 DeformableModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 ParametricDeformableModels . . . . . . . . . . . . . . . . . . . . . 21 GeometricDeformableModels . . . . . . . . . . . . . . . . . . . . . 23 2.3 MedicalImageRegistration . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 TextureandGrayLevelCo-OccurrenceMatrix . . . . . . . . . . . . 29 2.5 SoftComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Classificationproblems . . . . . . . . . . . . . . . . . . . . . . . . . 44 3 Datasets 47 3.1 MedicalImaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 iii CONTENTS iv 3.2 MicroscopyImages . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 ComputedTomographyImages . . . . . . . . . . . . . . . . . . . . . 52 3.4 MagneticResonanceImages . . . . . . . . . . . . . . . . . . . . . . 53 4 MedicalImageSegmentationusingDMsandSC 62 4.1 StatisticalShapeModels . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 LevelSetMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 PartII:ProposedMethods 90 5 HippocampusSegmentationusingASMsandRF 93 5.1 HistologicalImagesandHippocampus . . . . . . . . . . . . . . . . . 93 5.2 DE-basedhippocampuslocalization . . . . . . . . . . . . . . . . . . 98 BestReferenceSliceSelection . . . . . . . . . . . . . . . . . . . . . 98 HippocampusLocalization . . . . . . . . . . . . . . . . . . . . . . . 102 TargetFunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.3 SegmentationusingIterativeOtsu’sThresholdingMethodandRF . . 116 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 ExpansionoftheSegmentation . . . . . . . . . . . . . . . . . . . . . 118 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . 122 5.4 Real-worldapplication . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6 HippocampusSegmentationusingaMH-basedLSApproach 141 6.1 PreviousApproach . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 CONTENTS v 6.2 ProposedMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 TrainingPhase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 TestPhase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 6.3 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . 147 6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 7 BiomedicalISusingGeometricDMsandMHs 154 7.1 ProposedMethod . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Registration-basedprior . . . . . . . . . . . . . . . . . . . . . . . . . 157 Forceterms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 Parameterlearningusingmetaheuristics . . . . . . . . . . . . . . . . 160 7.2 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Methodsincludedinthecomparison . . . . . . . . . . . . . . . . . . 164 Parametersettings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Experimentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . 167 7.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 PartIII:FinalRemarks 180 8 FurtherWork 181 9 SummaryandConclusions 183 AppendixI:StatisticaltestsforanalyzingSCtechniquesbehaviour 187 AppendixII:StandardSegmentationMetrics 194 CONTENTS vi AppendixIII:PublicationsJuly2010-July2013 196 AppendixIV:ListofAbbreviations 200 Bibliography 204

Description:
It was in Spain that [my generation] learned that one can be right and yet be beaten, that force can vanquish spirit, that there are times when courage is not its own recompense. Albert Camus. English. First of all, I would like to thank Stefano Cagnoni for his dedication, patience, and constant co
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