Color medical image analysis PDF
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LectureNotesinComputationalVisionandBiomechanics Editors JoãoManuelR.S.Tavares R.M.NatalJorge Address: FaculdadedeEngenharia UniversidadedoPorto RuaDr.RobertoFrias,s/n 4200-465Porto Portugal [email protected],[email protected] EditorialAdvisoryBoard AlejandroFrangi,UniversityofSheffield,Sheffield,UK ChandrajitBajaj,UniversityofTexasatAustin,Austin,USA EugenioOñate,UniversitatPolitécnicadeCatalunya,Barcelona,Spain FranciscoPerales,BalearicIslandsUniversity,PalmadeMallorca,Spain GerhardA.Holzapfel,RoyalInstituteofTechnology,Stockholm,Sweden J.PauloVilas-Boas,UniversityofPorto,Porto,Portugal JeffreyA.Weiss,UniversityofUtah,SaltLakeCity,USA JohnMiddleton,CardiffUniversity,Cardiff,UK JoseM.GarcíaAznar,UniversityofZaragoza,Zaragoza,Spain PerumalNithiarasu,SwanseaUniversity,Swansea,UK KumarK.Tamma,UniversityofMinnesota,Minneapolis,USA LaurentCohen,UniversitéParisDauphine,Paris,France ManuelDoblaré,UniversidaddeZaragoza,Zaragoza,Spain PatrickJ.Prendergast,UniversityofDublin,Dublin,Ireland RainaldLöhner,GeorgeMasonUniversity,Fairfax,USA RogerKamm,MassachusettsInstituteofTechnology,Cambridge,USA ThomasJ.R.Hughes,UniversityofTexas,Austin,USA YongjieZhang,CarnegieMellonUniversity,Pittsburgh,USA YuboFan,BeihangUniversity,Beijing,China Forfurthervolumes: www.springer.com/series/8910 Lecture Notes in Computational Vision and Biomechanics Volume6 Theresearchrelatedtotheanalysisoflivingstructures(Biomechanics)hasbeenasourceofrecentre- searchinseveraldistinctareasofscience,forexample,Mathematics,MechanicalEngineering,Physics, Informatics, Medicine and Sport. However, for its successful achievement, numerous research top- icsshouldbeconsidered,suchasimageprocessingandanalysis,geometricandnumericalmodeling, biomechanics,experimentalanalysis,mechanobiologyandenhancedvisualization,andtheirapplica- tiontorealcasesmustbedevelopedandmoreinvestigationisneeded.Additionally,enhancedhardware solutionsandlessinvasivedevicesaredemanded. Ontheotherhand,ImageAnalysis(ComputationalVision)isusedfortheextractionofhighlevel informationfromstaticimagesordynamicimagesequences.Examplesofapplicationsinvolvingimage analysiscanbethestudyofmotionofstructuresfromimagesequences,shapereconstructionfrom imagesandmedicaldiagnosis.Asamultidisciplinaryarea,ComputationalVisionconsiderstechniques andmethodsfromotherdisciplines,suchasArtificialIntelligence,SignalProcessing,Mathematics, Physics and Informatics. Despite the many research projects in this area, more robust and efficient methodsofComputationalImagingarestilldemandedinmanyapplicationdomainsinMedicine,and theirvalidationinrealscenariosismatterofurgency. ThesetwoimportantandpredominantbranchesofScienceareincreasinglyconsideredtobestrongly connectedandrelated.Hence,themaingoaloftheLNCV&Bbookseriesconsistsoftheprovisionofa comprehensiveforumfordiscussiononthecurrentstate-of-the-artinthesefieldsbyemphasizingtheir connection.Thebookseriescovers(butisnotlimitedto): • ApplicationsofComputationalVisionand • GridandHighPerformanceComputingfor Biomechanics ComputationalVisionandBiomechanics • BiometricsandBiomedicalPatternAnalysis • Image-basedGeometricModelingandMesh • CellularImagingandCellularMechanics Generation • ClinicalBiomechanics • ImageProcessingandAnalysis • ComputationalBioimagingandVisualization • ImageProcessingandVisualizationin • ComputationalBiologyinBiomedicalImaging Biofluids • DevelopmentofBiomechanicalDevices • ImageUnderstanding • DeviceandTechniqueDevelopmentfor • MaterialModels BiomedicalImaging • Mechanobiology • DigitalGeometryAlgorithmsfor • MedicalImageAnalysis ComputationalVisionandVisualization • MolecularMechanics • ExperimentalBiomechanics • Multi-modalImageSystems • Gait&PostureMechanics • MultiscaleBiosensorsinBiomedicalImaging • MultiscaleAnalysisinBiomechanics • MultiscaleDevicesandBiomemsfor • NeuromuscularBiomechanics BiomedicalImaging • NumericalMethodsforLivingTissues • MusculoskeletalBiomechanics • NumericalSimulation • SportBiomechanics • SoftwareDevelopmentonComputational • VirtualRealityinBiomechanics VisionandBiomechanics • VisionSystems M. Emre Celebi (cid:2) Gerald Schaefer Editors Color Medical Image Analysis Editors M.EmreCelebi GeraldSchaefer ComputerScienceDepartment DepartmentofComputerScience LouisianaStateUniversityShreveport LoughboroughUniversity Shreveport,USA Loughborough,UK ISSN2212-9391 ISSN2212-9413(electronic) LectureNotesinComputationalVisionandBiomechanics ISBN978-94-007-5388-4 ISBN978-94-007-5389-1(eBook) DOI10.1007/978-94-007-5389-1 SpringerDordrechtHeidelbergNewYorkLondon LibraryofCongressControlNumber:2012948955 ©SpringerScience+BusinessMediaDordrecht2013 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerptsinconnection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’slocation,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer. PermissionsforusemaybeobtainedthroughRightsLinkattheCopyrightClearanceCenter.Violations areliabletoprosecutionundertherespectiveCopyrightLaw. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Whiletheadviceandinformationinthisbookarebelievedtobetrueandaccurateatthedateofpub- lication,neithertheauthorsnortheeditorsnorthepublishercanacceptanylegalresponsibilityforany errorsoromissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,withrespect tothematerialcontainedherein. Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface Sincetheearly20thcentury,medicalimaginghasbeendominatedbymonochrome imagingmodalitiessuchasx-ray,computedtomography,ultrasound,andmagnetic resonance imaging. As a result, color information has been overlooked in medi- calimageanalysisapplications.Recently,variousmedicalimagingmodalitiesthat involvecolorinformationhavebeenintroduced.Theseincludecervicography,der- moscopy,fundusphotography,gastrointestinalendoscopy,microscopy,andwound photography.However,incomparisontomonochromeimages,theanalysisofcolor medicalimagesisarelativelyunexploredarea.Themultivariatenatureofcolorim- age data presents new challenges for researchers and practitioners as conventional methodsdevelopedformonochromeimagesareoftennotdirectlyapplicabletomul- tichannelimages. The goal of this volume is to summarize the state-of-the-art in the utilization of color information in medical image analysis and provide future directions for this exciting subfield of medical image analysis. The intended audience includes researchers and practicing clinicians, who are increasingly using digital analytic tools. Thevolumeopenswith“ADataDrivenApproachtoCervigramImageAnalysis and Classification” by Kim and Huang. The authors describe an automated, data centricsystemforcervigramimageanalysisthatutilizescolorandtexturefeatures extractedfromtheregionsofinteresttoclassifyunseencasesusingaSupportVector Machineclassifiertrainedonseveralthousandannotatedimages.Theauthorsreport asensitivityof75%andaspecificityof76%onasetof2,000images. The volume continues with four chapters on skin lesion image analysis. In “Macroscopic Pigmented Skin Lesion Segmentation and Its Influence on Lesion ClassificationandDiagnosis,”CavalcantiandScharcanskiinvestigatetheinfluence ofsegmentationaccuracyonskinlesionclassification.Theimagesarefirstenhanced using a novel shading attenuation algorithm. Following a segmentation step, vari- ous shape, color, and texture related features are then extracted from the lesions. Finally,theimagesareclassifiedusinga1-NearestNeighbor(1-NN)classifier.The authors compare six recent monochromatic and multichannel segmentation meth- v vi Preface ods and conclude that the use of color information during segmentation improves theaccuracyofclassification. In“ColorandSpatialFeaturesIntegratedNormalizedDistanceforDensityBased BorderDetectioninDermoscopyImages,”Kockaraetal.proposeanimprovedden- sitybasedclusteringalgorithmfordetectinglesionbordersindermoscopyimages. ThisalgorithmisanacceleratedversionofthecelebratedDBSCAN(DensityBased Spatial Clustering of Applications with Noise) algorithm and it does not require any preprocessing. The authors obtain promising results on a difficult set of 100 dermoscopyimages. In “A Color and Texture Based Hierarchical K-NN Approach to the Classifica- tion of Non-Melanoma Skin Lesions,” Ballerini et al. describe a hierarchical clas- sificationsystemfornon-melanomaskinlesionsbasedontheK-NNclassifier.The images are first segmented using a region-based active contour model. Color and texturerelatedfeaturesarethenextractedfromthelesions.Finally,theimagesare classifiedusingahierarchicalK-NNclassifier.Theauthorsobtainpromisingresults onasetof960macroscopicimagesthatcontainsfiveclassesofnon-melanomaskin lesions. The final skin lesion analysis chapter, “Color Quantization of Dermoscopy Im- ages Using the K-Means Clustering Algorithm” by Celebi et al., investigates the applicability of a recently proposed k-means based color quantization method to dermoscopy images of skin lesions. This method improves upon conventional k- meansbasedcolorquantizationbyusingdatareduction,sampleweighting,acceler- atednearestneighborsearch,anddeterministicclustercenterinitialization.Theau- thorsdemonstratethattheirmethodoutperformsstate-of-the-artquantizationmeth- odswithrespecttodistortionminimization. In“GradingtheSeverityofDiabeticMacularEdemaCasesBasedonColorEye FundusImages,”Welferetal.presentanautomatedmethodfordetectingandgrad- ingdiabeticmacularedemasignsincoloreyefundusimages.First,theopticdisc, fovea center, and exudates are detected using a sequence of morphological opera- tors.Thespatialdistributionoftheexudatesaroundthemaculacenteristhenused toclassifyeachcaseintooneoffourcategories(absent,mild,moderate,andsevere) using a CART (Classification and Regression Trees) classifier. The authors obtain anaverageaccuracyofover94%onasetof89publiclyavailableimages. In “Colour Image Analysis of Wireless Capsule Endoscopy Video: A Review,” Fisher and Mackiewicz present a comprehensive survey of wireless capsule en- doscopy video analysis focusing on the related color imaging aspects. After pre- sentinganoverviewofthehistoryofthefield,theauthorsdiscussfeatureextraction, segmentation,significanteventdetection,andadaptivecontrolofviewingspeed. The volume continues with two chapters on microscopy. In “Automated Pro- totype Generation for Multi-Color Karyotyping,” Wu et al. present a three-step method for generating a prototype from multicolor karyotypes obtained via mul- tispectral imaging of human chromosomes. The first step involves the automated extraction of individual chromosomes from each karyotype, followed by chromo- some straightening and size normalization. In the second step, the extracted and normalized chromosomes belonging to each of the 24 color classes are automati- cally assigned to a particular group based on the ploidy level. Finally in the third Preface vii step,theprototypeofthecolorkaryotypeisdeterminedbygeneratingarepresenta- tivechromosomeforeachgroupusingpixel-basedfusion. Bueno et al. in “Colour Model Analysis for Histopathology Image Process- ing” compare five color models, namely Red-Green-Blue (RGB), Hue-Saturation- Intensity (HSI), Cyan-Magenta-Yellow-Black (CMYK), CIELAB, and Hue- Saturation-Density (HSD), for the analysis of histological whole slide images. Based on visual examination and Receiver Operating Characteristic (ROC) curve analysistheauthorsconcludethattheCIELABmodelgivesthebestresults. A chapter on burn image analysis entitled “A Review on CAD Tools for Burn Diagnosis” by Sáez et al. completes the volume. The authors discuss the issue of color normalization and then present a comparison of several color segmentation methodsappliedtoburnimages.Thechapterconcludeswithadiscussionofcolor basedestimationofburndepthusingaFuzzy-ARTMAPclassifier. Aseditors,wehopethatthisvolumefocusedonanalysisofcolormedicalimages will demonstrate the significant progress that has occurred in this field in recent years. We also hope that the developments reported in this volume will motivate furtherresearchinthisexcitingfield. M.EmreCelebi GeraldSchaefer Contents ADataDrivenApproachtoCervigramImageAnalysisandClassification 1 EdwardKimandXiaoleiHuang MacroscopicPigmentedSkinLesionSegmentationandItsInfluenceon LesionClassificationandDiagnosis . . . . . . . . . . . . . . . . . . . 15 PabloG.CavalcantiandJacobScharcanski ColorandSpatialFeaturesIntegratedNormalizedDistanceforDensity BasedBorderDetectioninDermoscopyImages . . . . . . . . . . . . 41 SinanKockara,MutluMete,andSaitSuer A Color and Texture Based Hierarchical K-NN Approach to the ClassificationofNon-melanomaSkinLesions . . . . . . . . . . . . . 63 LuciaBallerini,RobertB.Fisher,BenAldridge,andJonathanRees Color Quantization of Dermoscopy Images Using the K-Means ClusteringAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 M.EmreCelebi,QuanWen,SaeHwang,andGeraldSchaefer GradingtheSeverityofDiabeticMacularEdemaCasesBasedonColor EyeFundusImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 DanielWelfer, JacobScharcanski, PabloGautérioCavalcanti, DianeRuschelMarinho,LauraW.B.Ludwig,CleysonM.Kitamura, andMelissaM.DalPizzol ColourImageAnalysisofWirelessCapsuleEndoscopyVideo:AReview 129 MarkFisherandMichalMackiewicz AutomatedPrototypeGenerationforMulti-colorKaryotyping . . . . . . 145 XuqingWu,ShishirShah,andFatimaMerchant ColourModelAnalysisforHistopathologyImageProcessing . . . . . . . 165 GloriaBueno, OscarDéniz, JesúsSalido, M.MilagroFernández, NoeliaVállez,andMarcialGarcía-Rojo ix x Contents AReviewonCADToolsforBurnDiagnosis . . . . . . . . . . . . . . . . . 181 AuroraSáez,CarmenSerrano,andBegoñaAcha Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
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