ImageSegmentation
Principles,Techniques,andApplications
TaoLei
ShaanxiUniversityofScienceandTechnology
Xi’an,China
AsokeK.Nandi
BrunelUniversityLondon Uxbridge,UK
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To
Myparents,mywifeYanLu,andourdaughter LuLei. TaoLei
Mywife,Marion,andourchildren Robin,David,andAnitaNandi. AsokeK.Nandi
BriefContents
AbouttheAuthors xv
Preface xvii
Acknowledgment xix
ListofSymbolsandAbbreviations xxi
ListofAcronyms xxxi
PartIPrinciples 1
1Introduction 3
2Clustering 21
3MathematicalMorphology 37
4NeuralNetworks 63
PartIIMethods 97
5FastandRobustImageSegmentationUsingClustering 99
6FastImageSegmentationUsingWatershedTransform 125
7Superpixel-BasedFastImageSegmentation 151
PartIIIApplications 179
8ImageSegmentationforTrafficSceneAnalysis 181
9ImageSegmentationforMedicalAnalysis 199
10ImageSegmentationforRemoteSensingAnalysis 229
11ImageSegmentationforMaterialAnalysis 263
Index 289
Contents
AbouttheAuthors xv
Preface xvii
Acknowledgment xix
ListofSymbolsandAbbreviations xxi
ListofAcronyms xxxi
PartIPrinciples 1
1Introduction 3
1.1PreliminaryConcepts 3
1.2FoundationsofImageSegmentation 5
1.2.1Pixel-BasedImageSegmentation 5
1.2.2Contour-BasedImageSegmentation 6
1.2.3Region-BasedImageSegmentation 7
1.2.4NeuralNetwork–BasedImageSegmentation 8
1.3Examples:ImageSegmentation 9
1.3.1AutomaticDrive 9
1.3.2MedicalImageAnalysis 10
1.3.3RemoteSensing 10
1.3.4IndustrialInspection 11
1.4AssessmentofImageSegmentation 13
1.5DiscussionandSummary 15 References 17
2Clustering 21
2.1Introduction 21
2.2K-Means 22
2.3FuzzyC-meansClustering 24
2.4HierarchicalClustering 27
2.5SpectralClustering 29
2.6GaussianMixedModel 30
2.7DiscussionandSummary 33 References 34
3MathematicalMorphology 37
3.1Introduction 37
3.2MorphologicalFiltering 37
3.2.1ErosionandDilation 37
3.2.2OpeningandClosing 40
3.2.3BasicMorphologicalOperationforGrayscaleImages 41
3.2.4ComposedMorphologicalFilters 42
3.3MorphologicalReconstruction 43
3.3.1GeodesicDilationandErosion 43
3.3.2ReconstructionofOpeningOperationsandClosingOperations 45
3.4WatershedTransform 47
3.4.1BasicConcepts 47
3.4.2WatershedSegmentationAlgorithms 48
3.5MultivariateMathematicalMorphology 51
3.5.1RelatedConcepts 51
3.5.2DualityofGrayscaleMathematicalMorphology 51
3.5.3OrderingRelations 52
3.5.4MultivariateDualMorphologicalOperators 57
3.6DiscussionandSummary 60 References 60
4NeuralNetworks 63
4.1ArtificialNeuralNetworks 63
4.1.1Overview 63
4.1.2NeuronModel 65
4.1.3Single-LayerPerceptronandLinearNetwork 72
4.1.3.1SingleLayerPerceptron 72
4.1.3.2PerceptronLearningAlgorithm 73
4.1.3.3LinearNeuralNetwork 75
4.2ConvolutionalNeuralNetwork 76
4.2.1ConvolutionanditsApplicationinImages 76
4.2.1.1Definition 76
4.2.1.2One-DimensionalConvolutioninDiscreteDomain 77
4.2.1.3Two-DimensionalConvolutioninDiscreteDomain 78
4.2.1.4ExtendedConvolutionOperation 81
4.2.2ConvolutionalNetworkArchitectureandParameterLearning 83
4.2.2.1ConvolutionalNetworkArchitecture 83
4.2.2.2ConvolutionLayer 85
4.2.2.3PoolingLayer 86
4.2.2.4FullConnectionLayer 87
4.2.2.5ParameterLearning 88
4.2.2.6Back-PropagationAlgorithm 89
4.3GraphConvolutionalNetwork 90
4.3.1Overview 90
4.3.2ConvolutionalNetworkoverSpectralDomains 91
4.3.3ChebyshevNetwork 92
4.3.4GraphConvolutionalNetwork 93
4.4DiscussionandSummary 95 References 96
PartIIMethods 97
5FastandRobustImageSegmentationUsingClustering 99
5.1Introduction 99
5.2RelatedWork 101
5.2.1ObjectiveFunctionofFCMBasedonNeighborhoodInformation 102
5.2.2MembershipCorrectionBasedonLocalDistance 103
5.3LocalSpatialInformationIntegrationtoFCM 103
5.3.1FastandRobustFCMBasedonHistogram 104
5.3.2MorphologicalReconstruction 105
5.4MembershipFilteringforFCM 107
5.5DiscussionandSummary 111
5.5.1ResultsonSyntheticImages 111
5.5.2ResultsonRealImages 113
5.5.3ResultsonColorImages 116
5.5.4RunningTime 119
5.5.5Summary 121 References 121
6FastImageSegmentationUsingWatershedTransform 125
6.1Introduction 125
6.2RelatedWork 127
6.2.1MorphologicalOpeningandClosingReconstructions 127
6.2.2MultiscaleandAdaptiveMathematicalMorphology 128
6.2.3SeededSegmentation 129
6.2.4SpectralSegmentation 132
6.3AdaptiveMorphologicalReconstruction(AMR) 132
6.3.1ThePresentedAMR 132
6.3.2TheMonotonicIncreasing-nessPropertyofAMR 133
6.3.3TheConvergencePropertyofAMR 136
6.3.4TheAlgorithmofAMR 138
6.4AMRforSeededImageSegmentation 140
6.4.1SeededImageSegmentation 140
6.4.2Seed-BasedSpectralSegmentation 144
6.5DiscussionandSummary 147
6.5.1Discussion 147
6.5.2Summary 148 References 149
7Superpixel-BasedFastImageSegmentation 151
7.1Introduction 151
7.2RelatedWork 153
7.2.1FuzzyClusteringwithAdaptiveLocalInformation 153
7.2.2FCMBasedonHistogramofGrayImages 154
7.3SuperpixelIntegrationtoFCM 155
7.3.1SuperpixelBasedonLocalFeature 157
7.3.2Superpixel-BasedFastFCM 159
7.4DiscussionandSummary 164
7.4.1ComparisonwithOtherAlgorithms 164
7.4.2ParameterSetting 165
7.4.3ResultsonSyntheticImage 165
7.4.4ResultsonRealImages 167
7.4.5ExecutionTime 174
7.4.6Conclusions 174 References 176
PartIIIApplications 179
8ImageSegmentationforTrafficSceneAnalysis 181
8.1Introduction 181
8.2RelatedWork 182
8.2.1ConvolutionalNeuralNetworksforImageClassification 182
8.2.2TrafficSceneSemanticSegmentationUsingConvolutionalNeuralNetworks 183
8.3Multi-ScaleFeatureFusionNetworkforSceneSegmentation 185
8.3.1Multi-ScaleFeatureFusionUsingDilatedConvolution 186
8.3.2Encoder-DecoderArchitecture 186
8.3.3Experiments 187
8.4Self-AttentionNetworkforSceneSegmentation 188
8.4.1Non-localattentionModule 189
8.4.2DualAttentionModule 190
8.4.3Criss-CrossAttention 191
8.4.4Multi-scaleNon-localModule 193
8.4.5Experiments 193
8.5DiscussionandSummary 194
8.5.1NetworkArchitectureSearch 194
8.5.2CompactNetworks 195
8.5.3VisionTransformer 195 References 196
9ImageSegmentationforMedicalAnalysis 199
9.1Introduction 199
9.2RelatedWork 200
9.2.1TraditionalApproachesforMedicalImageSegmentation 200
9.2.2DeepLearningforMedicalImageSegmentation 200
9.3LightweightNetworkforLiverSegmentation 203
9.3.1NetworkCompression 203
9.3.23DDeepSupervision 205
9.3.3Experiment 206
9.3.3.1DataSetPreprocessing 206
9.3.3.2Training 207
9.3.3.3EvaluationandResults 207
9.4DeformableEncoder–DecoderNetworkforLiverandLiver-TumorSegmentation 208
9.4.1DeformableEncoding 209
9.4.2Ladder-ASPP 212
9.4.3LossFunction 214
9.4.4Postprocessing 215
9.4.5Experiment 216
9.4.5.1DataSetandPreprocessing 216
9.4.5.2ExperimentalSetupandEvaluationMetrics 216
9.4.5.3AblationStudy 217
9.4.5.4ExperimentalComparisononTestDataSets 219
9.4.5.5Model-SizeComparison 223
9.5DiscussionandSummary 224 References 224
10ImageSegmentationforRemoteSensingAnalysis 229
10.1Introduction 229
10.2RelatedWork 230
10.2.1ThresholdSegmentationMethods 230
10.2.2ClusteringSegmentationMethods 230
10.2.3RegionSegmentationMethods 231
10.2.4SegmentationMethodsUsingDeepLearning 231
10.3UnsupervisedChangeDetectionforRemoteSensingImages 232
10.3.1ImageSegmentationUsingImageStructuringInformation 232
10.3.2ImageSegmentationUsingGaussianPyramid 234
10.3.3FastFuzzy C-MeansforChangeDetection 236
10.3.4PostprocessingforChangeDetection 237
10.3.5TheProposedMethodology 241
10.3.6Experiments 242
10.3.6.1DataDescription 242
10.3.6.2ExperimentalSetup 243
10.3.6.3ExperimentalResults 245
10.3.6.4ExperimentalAnalysis 247
10.4End-to-EndChangeDetectionforVHRRemoteSensingImages 252
10.4.1MMRforImagePreprocessing 252
10.4.2PyramidPooling 253
10.4.3TheNetworkStructureofFCN-PP 254
10.4.4Experiments 255
10.4.4.1DataDescription 255
10.4.4.2ExperimentalSetup 256
10.4.4.3ExperimentalResults 256
10.4.4.4ExperimentalAnalysis 257
10.5DiscussionandSummary 259
References 259
11ImageSegmentationforMaterialAnalysis 263
11.1Introduction 263
11.2RelatedWork 264
11.2.1MetalMaterials 264
11.2.2FoamMaterials 265
11.2.3CeramicsMaterials 265
11.3ImageSegmentationforMetalMaterialAnalysis 266
11.3.1SegmentationofPorousMetalMaterials 267
11.3.2ClassificationofHoles 268
11.3.3ExperimentAnalysis 269
11.4ImageSegmentationforFoamMaterialAnalysis 272
11.4.1EigenvalueGradientClustering 272
11.4.2TheAlgorithm 274
11.4.3ExperimentAnalysis 276
11.5ImageSegmentationforCeramicsMaterialAnalysis 278
11.5.1Preprocessing 279
11.5.2RobustWatershedTransform 279
11.5.3ContourOptimization 282
11.5.4ExperimentAnalysis 284
11.6DiscussionandSummary 285 References 286
Index 289
AbouttheAuthors
TaoLei receivedaPhDininformationandcommunicationengineeringfromNorthwesternPolytechnical University,Xi’an,China,in2011.From2012to2014, hewasapostdoctoralresearchfellowwiththeSchool ofElectronicsandInformation,NorthwesternPolytechnicalUniversity,Xi’an,China.From2015to2016,he wasavisitingscholarwiththeQuantumComputation andIntelligentSystemsgroupatUniversityofTechnologySydney,Sydney,Australia.From2016to2019,he wasapostdoctoralresearchfellowwiththeSchoolof ComputerScience,NorthwesternPolytechnicalUniversity,Xi’an,China.Hehasauthoredandcoauthored 80+researchpaperspublishedinIEEETIP,TFS,TGRS, TGRSL,ICASSP,ICIP,andFG.
HeiscurrentlyaprofessorwiththeSchoolofElectronicInformationandArtificialIntelligence,ShaanxiUniversityofScienceandTechnology. Hiscurrentresearchinterestsincludeimageprocessing,patternrecognition,andmachinelearning. ProfessorLeiisanassociateeditorof FrontiersinSignalProcessing;heisalsoaguesteditorof RemoteSensing and IEEEJESTAR.HeisaseniormemberofIEEEandCCF.
AsokeK.Nandi receivedthedegreeofPhDinphysics fromtheUniversityofCambridge(TrinityCollege), Cambridge.Heheldacademicpositionsinseveraluniversities,includingOxford,ImperialCollegeLondon, Strathclyde,andLiverpool,aswellasFinlandDistinguishedProfessorshipinJyvaskyla(Finland).In2013 hemovedtoBrunelUniversityLondon,tobecome theChairandHeadofElectronicandComputer Engineering.
In1983ProfessorNandijointlydiscoveredthethree fundamentalparticlesknownasW+,W ,andZ0,providingtheevidencefortheunificationoftheelectromagneticandweakforces,forwhichtheNobel CommitteeforPhysicsin1984awardedtheprizeto histwoteamleadersfortheirdecisivecontributions.
Hiscurrentresearchinterestslieintheareasofsignalprocessingandmachinelearning,withapplicationstocommunications,geneexpressiondata,functionalmagneticresonancedata,machine conditionmonitoring,andbiomedicaldata.Hehasmademanyfundamentaltheoreticalandalgorithmiccontributionstomanyaspectsofsignalprocessingandmachinelearning.Hehasmuch expertiseinbigdata,dealingwithheterogeneousdataandextractinginformationfrommultiple datasetsobtainedindifferentlaboratoriesanddifferenttimes.ProfessorNandihasauthoredover 600technicalpublications,including260journalpapersaswellasfivebooks,entitled Condition MonitoringwithVibrationSignals (Wiley,2020), AutomaticModulationClassification:Principles, AlgorithmsandApplications (Wiley,2015), IntegrativeClusterAnalysisinBioinformatics (Wiley, 2015), BlindEstimationUsingHigher-OrderStatistics (Springer,1999),and AutomaticModulation RecognitionofCommunicationsSignals (Springer,1996).Theh-indexofhispublicationsis80(GoogleScholar)andhisErdösnumberis2.
ProfessorNandiisaFellowoftheRoyalAcademyofEngineering(UK)andofsevenother institutions.AmongthemanyawardshereceivedaretheInstituteofElectricalandElectronics Engineers(USA)HeinrichHertzAwardin2012,theGloryofBengalAwardforhisoutstanding achievementsinscientificresearchin2010,fromtheSocietyforMachineryFailurePrevention Technology,adivisionoftheVibrationInstitute(USA)in2000,theWaterArbitrationPrizeof theInstitutionofMechanicalEngineers(UK)in1999,andtheMountbattenPremiumoftheInstitutionofElectricalEngineers(UK)in1998.ProfessorNandiisanIEEEDistinguishedLecturer (2018–2019).ProfessorNandiistheFieldChiefEditorof FrontiersinSignalProcessing journal.
Imagesegmentationisoneofthemostchallengingfrontiertopicsincomputervision.Itprovidesan importantfoundationforimageanalysisandimagedescription,aswellasimageunderstanding. Thebasictaskofimagesegmentationistosegmentanimageintoseveralregionsthatarenonoverlapping,withtheseregionshavingaccurateboundaries.Thecurrenttaskofimagesegmentation notonlyrequiresaccurateregiondivisionbutalsorequiresalabeloutputondifferentregions,that is,semanticsegmentation.Withthedevelopmentofcomputervisionandartificialintelligence techniques,therolesandimportanceofimagesegmentationhavegrownsignificantly.Imagesegmentationhasbeenappliedwidelyinvariousfields,suchasindustryofdetection,intelligenttransportation,biologicalmedicine,agriculture,defense,andremotesensing.Atpresent,alargenumber ofimagesegmentationtechniqueshavebeenreported,andmanyofthemhavebeensuccessfully appliedtoactualproductdevelopment.However,thefastdevelopmentofimagingandartificial intelligencetechniquesrequiresimagesegmentationtodealwithincreasinglymorecomplextasks. Thesecomplextasksrequiremoreeffectiveandefficientimagesegmentationtechniques.Forthat reason,thereisagrowingbodyofliteratureresultingfromeffortsinresearchanddevelopmentby manyresearchgroupsaroundtheworld.Althoughtherearemanypublicationsonimagesegmentation,thereisonlyafewcollectionsofrecenttechniquesandmethodsdevotedtothefieldofcomputervision.
Thisbookattemptstosummarizeandimproveprinciples,techniques,andapplicationsofcurrent imagesegmentation.Itcanhelpresearchers,postgraduatestudents,andpracticingengineersfrom colleges,researchinstitutes,andenterprisestounderstandthefieldquickly.Firstly,basedonthis book,researcherscanquicklyunderstandthebasicprinciplesofimagesegmentationandrelated mathematicalmethodssuchasclustering,mathematicalmorphology,andconvolutionalneural networks.Secondly,basedonclassicimageprocessingandmachinelearningtheory,thebook introducesaseriousofrecentmethodstoachievefastandaccurateimagesegmentation.Finally, thebookintroducestheeffectofimagesegmentationinvariousapplicationscenariossuchastraffic,medicine,remotesensing,andmaterials.Inbrief,thebookaimstoinform,enthuse,andattract moreresearcherstoenterthefieldandthusdevelopfurtherimagesegmentationtheoryand applications.
Chapter1 isabriefintroductiontoimagesegmentationanditsapplicationsinvariousfields includingindustry,medicine,defense,andenvironment.Besides,anexampleispresentedtohelp readersunderstandimagesegmentationquickly.
Chapter2 isconcernedwithprinciplesofclustering.Threeclusteringapproachesthatareconcernedcloselywithimagesegmentation,arepresented,i.e. k-meansclustering,fuzzy c-meansclustering,spectralclustering,andgaussianmixedmodel.
Chapter3 isconcernedwithprinciplesofmathematicalmorphologysinceitisimportantin imageprocessing,especiallywatershedtransform,whichispopularforimagesegmentation.Inthis chapter,morphologicalfiltering,morphologicalreconstruction,andthewatershedtransformare presented.Besides,multivariatemathematicalmorphologyispresentedsinceitisimportantfor multichannelimageprocessing,whichcanhelpimagesegmentationformultichannelimages.
Chapter4 isconcernedwithprinciplesofneuralnetworkssincetheyareimportantinimage processing,especiallyconvolutionalneuralnetworks,whicharepopularforimagesegmentation. Inthischapter,artificialneuralnetworks,convolutionalneuralnetworks,andgraphconvolutional networksarepresented.
Chapter5 introducesafastimagesegmentationapproachbasedonfuzzyclustering.ThischapterillustratesrelatedworkswithimprovedFCMandpresentstwostrategies:localspatialinformationintegrationandmembershipfiltering,whichachievesbettersegmentationresults.
Chapter6 introducesafastandrobustimagesegmentationapproachbasedonthewatershed transform.Thischapterillustratesrelatedworkswithseededimagesegmentationandpresents anadaptivemorphologicalreconstructionmethodthatcanhelpthewatershedtransformto achievebettersegmentationresults.
Chapter7 introducesafastimagesegmentationapproachbasedonsuperpixelandtheGaussian mixedmodel(GMM).ThischapterillustratesrelatedworkswithsuperpixelalgorithmsandpresentstheideaofcombingsuperpixelandGMMforimagesegmentation.
Chapter8 introducestheapplicationofimagesegmentationfortrafficscenesegmentation.This chapterillustratesrelatedworkswithtrafficscenesemanticsegmentationandpresentstheideaof multi-scaleinformationfusioncombingnonlocalnetworkfortrafficscenesemanticsegmentation.
Chapter9 introducestheapplicationofimagesegmentationformedicalimages.Thischapter illustratesrelatedworkswithliverandliver-tumorsegmentationandpresentstwoapproaches forliverandliver-tumorsegmentationincludinglightweightV-netanddeformablecontextencodingnetwork.
Chapter10 introducestheapplicationofimagesegmentationforremotesensing.Thischapter illustratesrelatedworkswithchangedetectionandpresentstwoapproachesforchangedetection includingunsupervisedchangedetectionandend-to-endchangedetectionforveryhighresolution (VHR)remotesensingimages.
Chapter11 introducestheapplicationofimagesegmentationformaterialanalysis.Thischapter presentsthreeapplicationsfordifferentmaterialanalysisincludingmetallicmaterials,foammaterials,andceramicsmaterials.
Thisbookisup-to-dateandcoversalotoftheadvancedtechniquesusedforimagesegmentation, includingrecentlydevelopedmethods.Inaddition,thisbookprovidesnewmethods,including unsupervisedclustering,watershedtransform,anddeeplearningforimagesegmentation,which coversvarioustopicsofcurrentresearchinterest.Additionally,thebookwillprovideseveralpopularimagesegmentationapplicationsincludingtrafficscene,medicalimages,remotesensing images,andscanningelectronmicroscopeimages.Aworkofthismagnitudewill,unfortunately, containerrorsandomissions.Wewouldliketotakethisopportunitytoapologizeunreservedlyfor allsuchindiscretionsinadvance.Wewelcomecommentsandcorrections;pleasesendthemby emailtoa.k.nandi@ieee.orgorbyanyothermeans.
Acknowledgment
Itshouldberemarkedthatsomeoftheresearchresultsreportedinthisbookhavebeensourced fromrefereedpublications,arisingfromprojectsoriginallyfundedbytheRoyalSociety(UK)grant (IEC\NSFC\170396)andNSFC(China)grant(61811530325).
SEPTEMBER2022TAO LEIAND ASOKE K.NANDI
XI’AN,CHINA, AND LONDON,UK
