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Geometric Methods in Bio-Medical Image Processing -

Geometric Methods in Bio-Medical Image Processing

Ravikanth Malladi (Herausgeber)

Buch | Hardcover
VIII, 147 Seiten
2002 | 2002
Springer Berlin (Verlag)
978-3-540-43216-6 (ISBN)
CHF 149,75 inkl. MwSt
Visualization has become increasingly important for many types of biomedial applications. This book collects the latest results in the development of visualization methods in this field.
Itgivesmegreatpleasuretoeditthisbook. Thegenesisofthisbookgoes backtotheconferenceheldattheUniversityofBolognainJune1999,on collaborativeworkbetweentheUniversityofCaliforniaatBerkeleyandthe UniversityofBologna. Theoriginalideawastoinvitesomespeakersatthe conferencetosubmitarticlestothebook. Thescopeofthebookwaslater- hancedand,inthepresentform,itisacompilationofsomeoftherecentwork usinggeometricpartialdi?erentialequationsandthelevelsetmethodology inmedicalandbiomedicalimageanalysis. Thesynopsisofthebookisasfollows:Inthe?rstchapter,R. Malladi andJ. A. Sethianpointtotheoriginsoftheuseoflevelsetmethodsand geometricPDEsforsegmentation,andpresentfastmethodsforshapes- mentationinbothmedicalandbiomedicalimageapplications. InChapter 2,C. OrtizdeSolorzano,R. Malladi,andS. J. Lockettdescribeabodyof workthatwasdoneoverthepastcoupleofyearsattheLawrenceBerkeley NationalLaboratoryonapplicationsoflevelsetmethodsinthestudyand understandingofconfocalmicroscopeimagery. TheworkinChapter3byA. Sarti,C. Lamberti,andR. Malladiaddressestheproblemofunderstanding di?culttimevaryingechocardiographicimagery. Thisworkpresentsvarious levelsetmodelsthataredesignedto?tavarietyofimagingsituations,i. e. timevarying2D,3D,andtimevarying3D. InChapter4,L. VeseandT. F. Chanpresentasegmentationmodelwithoutedgesandalsoshowextensions totheMumford-Shahmodel. Thismodelisparticularlypowerfulincertain applicationswhencomparisonsbetweennormalandabnormalsubjectsis- quired. Next,inChapter5,A. EladandR. Kimmelusethefastmarching methodontriangulateddomaintobuildatechniquetounfoldthecortexand mapitontoasphere. Thistechniqueismotivatedinpartbynewadvances infMRIbasedneuroimaging. InChapter6,T. DeschampsandL. D. Cohen presentaminimalpathbasedmethodofgroupingconnectedcomponentsand showcleverapplicationsinvesseldetectionin3Dmedicaldata. Finally,in Chapter7,A. Sarti,K. Mikula,F. Sgallari,andC. Lamberti,describean- linearmodelfor?lteringtimevarying3Dmedicaldataandshowimpressive resultsinbothultrasoundandechoimages. IoweadebtofgratitudetoClaudioLambertiandAlessandroSartifor invitingmetoBologna,andlogisticalsupportfortheconference. Ithank thecontributingauthorsfortheirenthusiasmand?exibility,theSpringer mathematicseditorMartinPetersforhisoptimismandpatience,andJ. A. Sethianforhisunfailingsupport,goodhumor,andguidancethroughthe years. Berkeley,California R. Malladi October,2001 Contents 1 FastMethodsforShapeExtractioninMedicaland BiomedicalImaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 R. Malladi,J. A. Sethian 1. 1Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1. 2TheFastMarchingMethod. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1. 3ShapeRecoveryfromMedicalImages. . . . . . . . . . . . . . . . . . . . . . . . . . 6 1. 4Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2 AGeometricModelforImageAnalysisinCytology. . . . . . . 19 C. OrtizdeSolorzano,R. Malladi,,S. J. Lockett 2. 1Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2. 2GeometricModelforImageAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2. 3SegmentationofNuclei. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2. 4SegmentationofNucleiandCellsUsingMembrane-RelatedProtein Markers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2. 5Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3 LevelSetModelsforAnalysisof2Dand3D EchocardiographicData. . . . . . . . . . . . . . . .

1 Fast Methods for Shape Extraction in Medical and Biomedical Imaging.- 1.1 Introduction.- 1.2 The Fast Marching Method.- 1.3 Shape Recovery from Medical Images.- 1.4 Results.- References.- 2 A Geometric Model for Image Analysis in Cytology.- 2.1 Introduction.- 2.2 Geometric Model for Image Analysis.- 2.3 Segmentation of Nuclei.- 2.4 Segmentation of Nuclei and Cells Using Membrane-Related Protein Markers.- 2.5 Conclusions.- References.- 3 Level Set Models for Analysis of 2D and 3D Echocardiographic Data.- 3.1 Introduction.- 3.2 The Geometric Evolution Equation.- 3.3 The Shock-Type Filtering.- 3.4 Shape Extraction.- 3.5 2D Echocardiography.- 3.6 2D + time Echocardiography.- 3.7 3D Echocardiography.- 3.8 3D + time Echocardiography.- 3.9 Conclusions.- References.- 4 Active Contour and Segmentation Models using Geometric PDE's for Medical Imaging.- 4.1 Introduction.- 4.2 Description of the Models.- 4.3 Applications to Bio-Medical Images.- 4.4 Concluding Remarks.- References.- 5 Spherical Flattening of the Cortex Surface.- 5.1 Introduction.- 5.2 Fast Marching Method on Triangulated Domains.- 5.3 Multi-Dimensional Scaling.- 5.4 Cortex Unfolding.- 5.5 Conclusions.- References.- 6 Grouping Connected Components using Minimal Path Techniques.- 6.1 Introduction.- 6.2 Minimal Paths in 2D and 3D.- 6.3 Finding Contours from a Set of Connected Components Rk.- 6.4 Finding a Set of Paths in a 3D Image.- 6.5 Conclusion.- References.- 7 Nonlinear Multiscale Analysis Models for Filtering of 3D + Time Biomedical Images.- 7.1 Introduction.- 7.2 Nonlinear Diffusion Equations for Processing of 2D and 3D Still*Images.- 7.3 Space-Time Filtering Nonlinear Diffusion Equations.- 7.4 Numerical Algorithm.- 7.5 Discussion on Numerical Experiments.- 7.6 Preconditioning and Solving of Linear Systems.-References.- Appendix. Color Plates.

From the reviews:

R. Malladi (ed.)

Geometric Methods in Bio-Medical Image Processing

"This is an excellent monograph on geometric methods in biomedical image processing. I strongly recommend this book to visualization experts in mathematics, computer science and bio-medical applications and to research students on above topics."-JOURNAL OF COMPUTATIONAL METHODS IN APPLIED MATHEMATICS

"This book is based on the conference held at the University of Bologna at June 1999. ... The book gives a good review on some of the traditional applications in medical imagery (CT, MR, Ultrasound). ... This is an excellent monograph on geometric methods in biomedical image processing. I strongly recommend this book to visualization experts in mathematics, computer science and bio-medical applications and to research students on the above topics." (T. E. Simos, Journal of Computational Methods in Sciences and Engineering, Vol. 3 (2), 2003)

Erscheint lt. Verlag 23.4.2002
Reihe/Serie Mathematics and Visualization
Zusatzinfo VIII, 147 p.
Verlagsort Berlin
Sprache englisch
Maße 155 x 235 mm
Gewicht 424 g
Themenwelt Informatik Software Entwicklung User Interfaces (HCI)
Mathematik / Informatik Mathematik Graphentheorie
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Medizinische Fachgebiete Radiologie / Bildgebende Verfahren Radiologie
Schlagworte algorithm • Bildanalyse (EDV) • Biomedical Images • Calculus • Computed tomography (CT) • Computergestützte Bildgebende Verfahren (Medizin) • Cytometry • Diffusion • Echocardiography • filtering • Image Analysis • Image Processing • Level Sets • Life Sciences • Medical Imaging • Methodology • Protein • Ultrasound • Ultrasound Imagery
ISBN-10 3-540-43216-7 / 3540432167
ISBN-13 978-3-540-43216-6 / 9783540432166
Zustand Neuware
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