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Non-rigid Registration for Medical Images Using a Free-form Deformation

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Medical Imaging Technology

•  Growth of medical imaging devices •  Diagnosis using medical images

– Visualization – Computer Aided Diagnosis

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Diagnosis using multi-modal images •  Modality

–  Medical imaging Devices CT, MRI, PET, etc...

•  Features of each modality

CT X-Ray Structure MRI H atoms Tissue PET FDG-tracer Cancers

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Alignment of Multi modal images

•  Superimpose display – CT + PET

Defining the locations of the cancers

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+A =A

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Proposed deformation model

•  Multiple control grid: – Global grid

• entire image • rough alignment

– Local grid • observation area • accurate alignment

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Interaction between global and local control grids

•  Sequential operation <Step1> 2adjusting a global grid 2align the global area

<Step2> 2adjusting a local grid 2align the local area

registraPonA

registraPonA

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Modality CT (mono-modal) taken at different times

Resolution 152×200 Observation

area 79×94

Proposed B-Spline Control

grid Global 6×6 Local 6×6 11×11

order 5 3

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ToruATamaki,AJunkiAYoshimuta,AMisatoAKawakami,ABisserARaytchev,AKazufumiAKaneda,AShigetoAYoshida,AYoshitoATakemura,AKeiichiAOnji,ARieAMiyaki,AShinjiATanaka,AComputerMAidedAColorectalATumorAClassificaPonAinANBIAEndoscopyAUsingALocalAFeatures,AMedicalAImageAAnalysis,AAvailableAonlineA13ASeptemberA2012,AISSNA1361M8415,A10.1016/j.media.2012.08.003.AAYoshitoATakemura,AShigetoAYoshida,AShinjiATanaka,ARieAKawase,AKeiichiAOnji,AShiroAOka,AToruATamaki,ABisserARaytchev,AKazufumiAKaneda,AMasaharuAYoshihara,AKazuakiAChayama,AComputerMaidedAsystemAforApredicPngAtheAhistologyAofAcolorectalAtumorsAbyAusingAnarrowMbandAimagingAmagnifyingAcolonoscopyA(withAvideo),AGastrointesPnalAEndoscopy,AVolumeA75,AIssueA1,AJanuaryA2012,APagesA179M185,AISSNA0016M5107,A10.1016/j.gie.2011.08.051.AKeiichiAOnji,AShigetoAYoshida,AShinjiATanaka,ARieAKawase,AYoshitoATakemura,AShiroAOka,AToruATamaki,ABisserARaytchev,AKazufumiAKaneda,AMasaharuAYoshihara,AKazuakiAChayama,AQuanPtaPveAanalysisAofAcolorectalAlesionsAobservedAonAmagnifiedAendoscopyAimages,AJournalAofAGastroenterology,AVolumeA46,ANumberA12,A1382M1390,A2011.AA`ŉŖĸy,AŐĄÄ,ABisserARaytchev,AȂŖfò,AŽĈj�,A`ŖÕ�,AŖ�$Įȣ:AȽ�Ʃà�4ǃȆŗ+ɞNBIà�×ǁ;ȟɝyɗɇŋÃȁɚǖ?kɞǋǈȾ,Aȗ�Ízǯ$��ÙƻzdȺʒʇʫʩǑǖʪʟʌɳɲœǇŮŹ�ȣPRMU2011M3,AVol.111,ANo.47,App.13M18,AbY¡��,AÐūȣ(2011A05).AToruATamaki,AJunkiAYoshimuta,ATakahishiATakeda,ABisserARaytchev,AKazufumiAKaneda,AShigetoAYoshida,AYoshitoATakemura,AShinjiATanaka:A"AAsystemAforAColorectalATumorAClassificaPonAinAMagnifyingAEndoscopicANBIAImages,"AProc.AofAACCV2010A;ATheA10thAAsianAConferenceAonAComputerAVision,AVol.2,App.987M998A(2010A11),AQueenstown,ANewAZealand,ANovemberA8M12,A2010.A`ŉŖĸy,AŽŖ�ů[,AŐĄÄ,ABisserARaytchev,AȂŖfò,AŽĈj�,A`ŖÕ�,AŖ�$Įȣ:AȽDenseASIFTɱŕɀɓ�ƩƨŞNBIŗ+ǑǖȾ,Aȗ�Ízǯ$��ÙƻzdʒʇʫʩǑǖʪʟʌɳɲœǇŮŹ�ȣPRMU2010M73,AVol.110,ANo.187,App.129M134,Aų¤��,Aų¤(2010A09).AAYoshitoATakemura,AShigetoAYoshida,AShinjiATanaka,AKeiichiAOnji,AShiroAOka,AToruATamaki,AKazufumiAKaneda,AMasaharuAYoshihara,AKazuakiAChayama:A"QuanPtaPveAanalysisAandAdevelopmentAofAaAcomputerMaidedAsystemAforAidenPficaPonAofAregularApitApa"ernsAofAcolorectalAlesions,"AGastrointesPnalAEndoscopy,AVol.A72,ANo.A5,App.A1047M1051A(2010A11).AMasashiAHIROTA,AToruATamaki,AKazuhumiAKaneda,AShigetoAYosida,AShinjiATanaka:A"FeatureAextracPonAfromAimagesAofAendoscopicAlargeAintesPne"AProc.AofAFCV2008A;AtheA14thAKoreaMJapanAJointAWorkshopAonAFronPersAofAComputerAVision,App.94M99A(2008A01)A

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YoshitoATakemura,AShigetoAYoshida,AShinjiATanaka,AKeiichiAOnji,AShiroAOka,AToruATamaki,AKazufumiAKaneda,AMasaharuAYoshihara,AKazuakiAChayama:A"QuanPtaPveAanalysisAandAdevelopmentAofAaAcomputerMaidedAsystemAforAidenPficaPonAofAregularApitApa"ernsAofAcolorectalAlesions,"AGastrointesPnalAEndoscopy,AVol.A72,ANo.A5,App.A1047M1051A(2010A11).A

Outline of Our Approach�

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•  Dataset

–  908 NBI images (Type A: 359, Type B: 462, Type C3: 87)

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•  Dataset

–  908 NBI images (Type A: 359, Type B: 462, Type C3: 87)

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–  ǖ?kȺȺȺȺ: SVM (linear kernel)

•  Dataset

–  1412 NBI images: 908 training images

504 test images

•  ǎ": Holdout Testing –  �ƞŗ+ʪʋʃʍŗ+ɱq�ɍɘǎ"

–  # of visual-words: 3×22, 3×23, …, 3×213

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Results <Holdout Testing>�

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Conclusions�

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Key Idea : ʤʙʦɞɜɀțxÍzɱŕɀɮ

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広島大学工学部における医用画像処理の取り組み：放射線科，眼科，内科

Health & Medicine