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E-raamat: Visual Computing for Medicine: Theory, Algorithms, and Applications

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This second edition reflects the developments in medical imaging, algorithm processing, and applications in medical research and clinical use since 2006. In addition to rewriting all the original chapters, they have added a number of new ones and reorganized and updated others. They cover the acquisition, analysis, and interpretation of medical volume data; the visualization and exploration of medical volume data; advanced medical visualization techniques; the visualization of high-dimensional medical image data; and treatment planning, guidance, and training. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Visual Computing for Medicine, Second Edition, offers cutting-edge visualization techniques and their applications in medical diagnosis, education, and treatment. The book includes algorithms, applications, and ideas on achieving reliability of results and clinical evaluation of the techniques covered. Preim and Botha illustrate visualization techniques from research, but also cover the information required to solve practical clinical problems. They base the book on several years of combined teaching and research experience. This new edition includes six new chapters on treatment planning, guidance and training; an updated appendix on software support for visual computing for medicine; and a new global structure that better classifies and explains the major lines of work in the field.

  • Complete guide to visual computing in medicine, fully revamped and updated with new developments in the field
  • Illustrated in full color
  • Includes a companion website offering additional content for professors, source code, algorithms, tutorials, videos, exercises, lessons, and more

Arvustused

" I highly recommend it for one-semester advanced graduate courses in computer graphics. For graduate students pursuing PhDs and professionals in research and development in the medical visualization filed, this book is well worth reading." --Computing Reviews, November 2014

Muu info

Fully revamped and updated to broaden the scope from pure visualization to visual computing, this new edition of a key book by medical visualization luminaries offers cutting-edge visualization techniques and applications for medical diagnosis and treatment.
Acknowledgments xvii
Foreword To The Second Edition xix
Preface To The Second Edition xxi
Author Biography xxiii
01 Introduction
1(14)
1.1 Visualization in Medicine as a Specialty of Scientific Visualization
1(2)
1.2 Computerized Medical Imaging
3(4)
1.3 2D and 3D Visualizations
7(1)
1.4 Further Information
8(1)
1.5 Organization
9(6)
PART I ACQUISITION, ANALYSIS, AND INTERPRETATION OF MEDICAL VOLUME DATA
02 Acquisition Of Medical Image Data
15(54)
2.1 Introduction
15(1)
2.2 Medical Image Data
16(3)
2.3 Data Artifacts and Signal Processing
19(5)
2.3.1 Sampling Theorem
19(2)
2.3.2 Undersampling and Aliasing
21(1)
2.3.3 Interpolation Artifacts
22(2)
2.4 X-Ray Imaging
24(5)
2.4.1 Angiography
26(1)
2.4.2 Rotational X-Ray
26(2)
2.4.3 Discussion
28(1)
2.4.4 Current and Future Developments of X-Ray Imaging
28(1)
2.5 Computed Tomography
29(11)
2.5.1 Computed Tomography Compared to X-Ray Imaging
30(1)
2.5.2 Principle of CT Data Generation
30(1)
2.5.3 Standardization with Hounsfield Units
31(1)
2.5.4 Parameters of CT Scanning
32
2.5.5 Artifacts in CT Image Acquisition
3(34)
2.5.6 Current and Future Developments of CT Scanners
37(3)
2.5.7 Discussion
40(1)
2.6 Magnetic Resonance Imaging
40(12)
2.6.1 Principles of MRI
41(3)
2.6.2 Parameters of MR Scanning
44(3)
2.6.3 Artifacts in MRI Data
47(1)
2.6.4 Functional MRI
48(2)
2.6.5 Ultra-High-Field MRI
50(1)
2.6.6 Diffusion Tensor Imaging
51(1)
2.6.7 Discussion
52(1)
2.7 Ultrasound
52(3)
2.8 Imaging in Nuclear Medicine
55(6)
2.8.1 Positron Emission Tomography---PET
56
2.8.2 Hybrid PET/CT and PET/MRI Scanners
5(55)
2.8.3 Single Photon Emission Computed Tomography---SPECT
60(1)
2.9 Intraoperative Imaging
61(4)
2.9.1 CT- and MR-Guided Interventions
62(1)
2.9.2 Fluoroscopy
63(1)
2.9.3 Intraoperative Ultrasound
63(1)
2.9.4 Intraoperative MRI
64(1)
2.10 Summary
65(4)
03 An Introduction To Medical Visualization In Clinical Practice
69(42)
3.1 Introduction
69(1)
3.2 Diagnostic Accuracy
70(3)
3.3 Visual Perception
73(8)
3.3.1 Gray Value Perception
73(4)
3.3.2 Color Spaces, Color Scales, and Color Perception
77(4)
3.3.3 Visual Perception and Attention in the Diagnosis Of Medical Volume Data
81(1)
3.4 Storage of Medical Image Data
81(2)
3.4.1 Scope of Dicom
82(1)
3.4.2 Structure of Dicom Data
82(1)
3.5 Conventional Film-Based Diagnosis
83(3)
3.5.1 Cooperation of Radiologists and Radiology Technicians
85(1)
3.5.2 Tasks in Conventional Film-Based Diagnosis
85(1)
3.6 Soft-Copy Reading
86(15)
3.6.1 Digital Radiology Departments
86(2)
3.6.2 Tasks in Soft-Copy Reading
88(4)
3.6.3 Digital Hanging Protocol
92(1)
3.6.4 Computer-Aided Detection
93(4)
3.6.5 Diagnosis with 3D Visualizations
97(3)
3.6.6 Guidelines for Soft-Copy Reading
100(1)
3.7 Medical Visualization in Nuclear Medicine
101(1)
3.8 Medical Image Data in Radiation Treatment Planning
102(5)
3.8.1 Conformant and Intensity-Modulated Radiation Treatment
105(2)
3.8.2 Brachytherapy
107(1)
3.9 Medical Team Meetings
107(2)
3.10 Concluding Remarks
109(2)
04 Image Analysis For Medical Visualization
111(66)
4.1 Introduction
111(1)
4.2 Preprocessing and Filtering
112(12)
4.2.1 ROI Selection
113(1)
4.2.2 Resampling
113(1)
4.2.3 Histogram and Histogram Transformation
114(2)
4.2.4 General Noise Reduction Techniques
116(6)
4.2.5 Inhomogeneity Correction
122(1)
4.2.6 Gradient Filtering
123(1)
4.3 An Introduction to Image Segmentation
124(8)
4.3.1 Requirements
125(1)
4.3.2 Manual Segmentation
125(1)
4.3.3 Threshold-Based Segmentation
126(2)
4.3.4 Region Growing
128(1)
4.3.5 Watershed Segmentation
129(3)
4.4 Graph-Based Segmentation Techniques
132(7)
4.4.1 Livewire Segmentation
132(4)
4.4.2 Contour-Based Segmentation with Variational Interpolation
136(1)
4.4.3 Graph Cuts
137(1)
4.4.4 Random Walker Segmentation
138(1)
4.5 Advanced and Model-Based Segmentation Methods
139(14)
4.5.1 Active Contour Models
140(1)
4.5.2 Level Sets and Fast Marching Methods
141(2)
4.5.3 Statistical Shape Models
143(3)
4.5.4 Active Appearance Models
146(2)
4.5.5 Incorporating Model Assumptions in Region Growing Segmentation
148(1)
4.5.6 Application: Tumor Segmentation
148
4.5.7 Verification and Representation of Segmentation Results
15(138)
4.6 Interaction for Segmentation
153(9)
4.6.1 General Techniques for Correcting Pre-Segmentations
155(1)
4.6.2 Mesh-Based Correction of Segmentation Results
155(4)
4.6.3 Interactive Morphological Image Processing
159(1)
4.6.4 Interaction Techniques for Semi-Automatic Segmentation
160(2)
4.7 Validation of Segmentation Methods
162(3)
4.7.1 Phantom Studies Versus Clinical Data
162(1)
4.7.2 Validation Metrics
163(1)
4.7.3 Validation with Public Databases
164(1)
4.8 Registration and Fusion of Medical Image Data
165(7)
4.8.1 Transformation
166(1)
4.8.2 Fitting
167(2)
4.8.3 Model-Based Registration
169(1)
4.8.4 Efficient Registration
170(1)
4.8.5 Visualization
170(2)
4.9 Summary
172(5)
05 Human-Computer Interaction For Medical Visualization
177(52)
5.1 Introduction
177(2)
5.2 User and Task Analysis
179(12)
5.2.1 Task Analysis Methods
179(2)
5.2.2 What has to be Analyzed?
181(1)
5.2.3 Representations of Task Analysis
181(8)
5.2.4 Understanding the User
189
5.2.5 Case Study: Task Analysis for Medical Team Meetings
19(172)
5.3 Metaphors
191(2)
5.4 Prototyping
193(1)
5.5 User Interface Principles and User Experience
194(5)
5.5.1 General User Interface Principles
195(2)
5.5.2 User Interface Principles for Medical Applications
197(1)
5.5.3 User Experience
198(1)
5.6 3D Interaction Techniques
199(6)
5.6.1 Selection Tasks
199(1)
5.6.2 3D Rotation
200(2)
5.6.3 Object Placement
202(1)
5.6.4 Navigation
203(2)
5.7 Input Devices
205(7)
5.7.1 6 Dof Input Devices
207(3)
5.7.2 Tactile Input Devices
210(2)
5.8 HCI in the Operating Room
212(4)
5.9 Mobile Computing
216(4)
5.10 Evaluation
220(3)
5.10.1 Formative and Summative Evaluations
221(1)
5.10.2 Inspection-Based and Empirical Evaluations
222(1)
5.10.3 Evaluation of Interactive Segmentation Techniques
222(1)
5.10.4 Post Market Clinical Follow Up
223(1)
5.11 Conclusion
223(6)
PART II VISUALIZATION AND EXPLORATION OF MEDICAL VOLUME DATA
06 Surface Rendering
229(40)
6.1 Introduction
229(1)
6.2 Reconstruction of Surfaces from Contours
230(3)
6.2.1 Topological Problems
230(1)
6.2.2 Neighborhood Relations in Surface Meshes
231(1)
6.2.3 Representation of Surface Meshes
232(1)
6.3 Marching Cubes
233(8)
6.3.1 Marching Squares
234(1)
6.3.2 Basic Algorithm
234(3)
6.3.3 Discussion
237(3)
6.3.4 Advanced Surface Extraction Methods
240(1)
6.3.5 Hardware-Accelerated Isosurface Extraction
241(1)
6.4 Surface Rendering of Unsegmented Volume Data
241(6)
6.4.1 Preprocessing Volume Data for Visualization
242(2)
6.4.2 Selection of Isovalues
244(1)
6.4.3 Multiple and Nested Isosurfaces
245(1)
6.4.4 Isosurface Topology Simplification
246(1)
6.5 Surface Rendering of Segmented Volume Data
247(11)
6.5.1 Preprocessing
248(2)
6.5.2 Basic Mesh Smoothing
250(4)
6.5.3 Interactive Real-Time Mesh Smoothing
254(3)
6.5.4 Evaluation of Smoothing Approaches
257(1)
6.6 Advanced Mesh Smoothing
258(4)
6.6.1 Constrained Mesh Smoothing
258(1)
6.6.2 Context-Aware Smoothing
259(2)
6.6.3 Extracting Surfaces from Label Volumes
261(1)
6.6.4 Evaluation of Advanced Mesh Smoothing
262(1)
6.7 Mesh Simplification and Web-Based Surface Rendering
262(4)
6.7.1 Mesh Simplification
263(1)
6.7.2 Web-Based Surgical Planning
264(1)
6.7.3 Web-Based Medical Education
265(1)
6.8 Concluding Remarks
266(3)
07 Direct Volume Visualization
269(20)
7.1 Theoretical Models
269(4)
7.1.1 Emission
270(1)
7.1.2 Absorption
271(1)
7.1.3 Volume Rendering Equation
271(2)
7.2 The Volume Rendering Pipeline
273(2)
7.2.1 Preclassified Volume Rendering Pipeline
274(1)
7.3 Compositing
275(7)
7.3.1 Compositing Variations: Pseudo X-Ray, MIP, CVP, and MIDA
277(2)
7.3.2 Thin Slab Volume Rendering
279(2)
7.3.3 Pre-Integrated Volume Rendering
281(1)
7.4 Volume Raycasting
282(1)
7.5 Efficient Volume Rendering
283(1)
7.6 Direct Volume Rendering on the GPU
284(2)
7.7 Summary
286(3)
08 Advanced Direct Volume Visualization
289(34)
8.1 Introduction
289(1)
8.2 Volumetric Illumination
290(24)
8.2.1 Volumetric Illumination Model
291(2)
8.2.2 Algorithm Classification
293(3)
8.2.3 Local Region-Based Techniques
296(4)
8.2.4 Slice-Based Techniques
300(3)
8.2.5 Light Space-Based Techniques
303(4)
8.2.6 Lattice-Based Techniques
307(3)
8.2.7 Basis Function-Based Techniques
310(2)
8.2.8 Raytracing-Based Techniques
312(1)
8.2.9 Perceptual Impact
313(1)
8.2.10 Technical Considerations
314(1)
8.3 Artificial Depth Enhancements
314(7)
8.3.1 Color-Coding
316(2)
8.3.2 Halos
318(2)
8.3.3 Depth of Field
320(1)
8.4 Concluding Remarks
321(2)
09 Volume Interaction
323(46)
9.1 Introduction
323(1)
9.2 One-Dimensional Transfer Functions
324(13)
9.2.1 Unassisted Techniques
326(1)
9.2.2 Data-Driven Transfer Functions
327(7)
9.2.3 Image-Driven Transfer Functions
334(3)
9.3 Multidimensional Transfer Functions
337(5)
9.3.1 Histograms for 2D Transfer Functions
337(2)
9.3.2 2D Component Functions
339(1)
9.3.3 Representation of 2D Transfer Functions
339(1)
9.3.4 Size-Based Transfer Functions
340(2)
9.4 Gradient-Based and LH-Based Transfer Functions
342(7)
9.4.1 Gradient-Based Transfer Functions
342(1)
9.4.2 Gradient Estimation and Storage
342(1)
9.4.3 User Interfaces for Gradient-Based Transfer Functions
342(4)
9.4.4 2D Transfer Functions Based on LH Histograms
346(3)
9.5 Local and Distance-Based Transfer Functions
349(4)
9.5.1 Distance-Based Transfer Functions
350(2)
9.5.2 Local Transfer Functions
352(1)
9.6 Advanced Picking
353(3)
9.6.1 Contextual Picking
354(1)
9.6.2 Visibility-Based Picking
355(1)
9.7 Clipping
356(2)
9.8 Virtual Resection
358(7)
9.8.1 Virtual Resections by Drawing on Slices
359(1)
9.8.2 Virtual Resection with a Deformable Cutting Plane
359(6)
9.9 Cutting Medical Volume Data
365(1)
9.9.1 High-Quality Representation of Cut Surfaces
366(1)
9.9.2 Virtual Resection and Surgery Simulation
366(1)
9.10 Summary
366(3)
10 Labeling And Measurements In Medical Visualization
369(32)
10.1 Introduction
369(1)
10.2 General Design Issues
370(1)
10.3 Interactive Measurement of Distances and Volumes
371(5)
10.3.1 Interactive Distance Measurement
371(2)
10.3.2 Estimation of Quantitative Values
373(3)
10.4 Automatic Distance Measures
376(8)
10.4.1 Bounding Volumes and Spatial Trees for Distance Computation
376(2)
10.4.2 Efficient and Flexible Distance Computation
378(3)
10.4.3 Clinical Examples
381(1)
10.4.4 Measuring the Extents of Objects
381(3)
10.5 Angular Measurements
384(3)
10.5.1 Measurement of Angles Between Elongated Objects
384(1)
10.5.2 Medical Applications
385(2)
10.6 Measurements in Virtual Reality
387(1)
10.7 Labeling 2D and 3D Medical Visualizations
387(10)
10.7.1 Internal Labeling of 3D Medical Surface Models
390(1)
10.7.2 External Labeling
391(3)
10.7.3 Labeling Slice-Based Visualizations
394(3)
10.8 Summary
397(4)
PART III ADVANCED MEDICAL VISUALIZATION TECHNIQUES
11 Visualization Of Vascular Structures
401(50)
11.1 Introduction
401(1)
11.2 Enhancing Vascular Structures
402(3)
11.2.1 Emphasis of Elongated Structures
402(1)
11.2.2 Bone Removal
403(2)
11.3 Projection-Based Visualization
405(7)
11.3.1 Maximum Intensity and Closest Vessel Projection
405(2)
11.3.2 Maximum Intensity Difference Accumulation
407(1)
11.3.3 Curved Planar Reformation
408(4)
11.4 Vessel Analysis
412(7)
11.4.1 Vessel Segmentation
412(2)
11.4.2 Skeletonization and Graph Analysis
414(4)
11.4.3 Diameter Estimation
418(1)
11.5 Model-Based Surface Visualization
419(13)
11.5.1 Reconstruction with Cylinders and Truncated Cones
420(4)
11.5.2 Visualization with Parametric and Subdivision Surfaces
424(1)
11.5.3 Implicit Reconstruction of Vascular Trees
425(7)
11.6 Model-Free Surface Visualization
432(6)
11.6.1 Smoothing Surface Visualizations
432(1)
11.6.2 Visualization with MPU Implicits
432(4)
11.6.3 Implicit Reconstruction with Sweeping
436(2)
11.7 Vessel Visualization for Diagnosis
438(10)
11.7.1 Diagnosis of Cerebral Aneurysms and Arterio-Venous Malformations
440(5)
11.7.2 Diagnosis of the Coronary Heart Disease
445(3)
11.7.3 Multiple Coordinated Views
448(1)
11.8 Summary
448(3)
12 Illustrative Medical Visualization
451(58)
12.1 Introduction
451(2)
12.2 Medical Applications
453(1)
12.3 Curvature Approximation
454(3)
12.3.1 Curvature-Related Measures
455(1)
12.3.2 Curvature Estimation for Illustrative Visualization
456(1)
12.4 An Introduction to Feature Lines
457(5)
12.4.1 An Overview of Feature Lines
458(1)
12.4.2 General Aspects of Feature Line Rendering
459(3)
12.5 Geometry-Dependent Feature Lines
462(14)
12.5.1 Silhouette Generation
462(5)
12.5.2 Crease Lines
467(1)
12.5.3 Ridge and Valley Lines
468(3)
12.5.4 Suggestive Contours
471(1)
12.5.5 Apparent Ridges
472(2)
12.5.6 Streamline-Based Illustrative Rendering
474(2)
12.6 Light-Dependent Feature Lines
476(6)
12.6.1 Laplacian Lines
476(1)
12.6.2 Photic Extremum Lines
477(3)
12.6.3 Highlight Lines
480(1)
12.6.4 Discussion
481(1)
12.7 Stippling
482(3)
12.7.1 Essential Parameters of Stippling
482(1)
12.7.2 Frame-Coherent Stippling
483(2)
12.8 Hatching
485(7)
12.8.1 Curvature-Guided Hatching
487(1)
12.8.2 Model-Based Hatching of Muscles and Vascular Structures
488(2)
12.8.3 Combination of Curvature and Preferential Direction
490(1)
12.8.4 Hatching Volume Models
491(1)
12.9 Illustrative Shading
492(8)
12.9.1 Shading in Medical Textbooks
493(1)
12.9.2 Realization of the Extended Shading
494(3)
12.9.3 Illustrative Visualization of Vascular Trees
497(3)
12.10 Smart Visibility
500(7)
12.10.1 Cutaways
501(4)
12.10.2 Ghosted Views
505(2)
12.11 Conclusion
507(2)
13 Virtual Endoscopy
509(28)
13.1 Introduction
509(1)
13.2 Medical and Technical Background
510(3)
13.3 Preprocessing
513(2)
13.3.1 Preprocessing Workflow
513(1)
13.3.2 Path Planning
513(2)
13.4 Rendering for Virtual Endoscopy
515(6)
13.4.1 Indirect Volume Rendering
515(2)
13.4.2 Direct Volume Rendering
517(1)
13.4.3 Hybrid Rendering
518(1)
13.4.4 Advanced Rendering
518(1)
13.4.5 Geometry Culling
518(3)
13.5 User Interfaces for Virtual Endoscopy
521(4)
13.5.1 Camera Control and Navigation
522(1)
13.5.2 Views for Interactive Virtual Endoscopy
523(1)
13.5.3 Graphical User Interface
524(1)
13.5.4 Input Devices
524(1)
13.6 Applications
525(11)
13.6.1 Virtual Colonoscopy
525(4)
13.6.2 Virtual Bronchoscopy
529(2)
13.6.3 Virtual Angioscopy
531(2)
13.6.4 Virtual Endoscopy for Minimally-Invasive Neurosurgery
533(3)
13.7 Concluding Remarks
536(1)
14 Projections And Reformations (Online
Chapter)
537(4)
PART IV VISUALIZATION OF HIGH-DIMENSIONAL MEDICAL IMAGE DATA
15 Visualization Of Brain Connectivity
541(48)
15.1 Introduction
541(2)
15.2 Acquisition of Connectivity Data
543(4)
15.2.1 EEG and MEG
543(1)
15.2.2 Magnetic Resonance Imaging
543(2)
15.2.3 Diffusion MRI
545(2)
15.2.4 Functional MRI
547(1)
15.3 Visualization of Structural Connectivity
547(32)
15.3.1 Scalar Reduction
548(6)
15.3.2 Glyphs
554(5)
15.3.3 Global Multifield
559(20)
15.4 Visualization of Connectivity Matrices
579(8)
15.4.1 Non-Spatial Methods
580(3)
15.4.2 Spatial Methods
583(4)
15.5 Summary
587(2)
16 Visual Exploration And Analysis Of Perfusion Data (Online
Chapter)
589(4)
PART V TREATMENT PLANNING, GUIDANCE AND TRAINING
17 Computer-Assisted Surgery
593(32)
17.1 Introduction
593(1)
17.2 General Tasks
594(1)
17.3 Visualization Techniques
595(12)
17.3.1 Visual Representation
596(2)
17.3.2 Interaction
598(1)
17.3.3 Simulation
598(3)
17.3.4 Quantitative Visualization
601(6)
17.4 Guidance Approaches
607(5)
17.4.1 Mental Model
608(1)
17.4.2 Documentation
609(1)
17.4.3 Image-Based Guidance
610(1)
17.4.4 Mechanical Guidance
610(2)
17.5 Application Areas
612(11)
17.5.1 Oral and Maxillofacial Surgery
612(2)
17.5.2 Orthopedic Surgery
614(2)
17.5.3 Neurosurgery
616(5)
17.5.4 Hepatic Surgery
621(2)
17.6 Conclusions
623(2)
18 Image-Guided Surgery And Augmented Reality
625(40)
18.1 Introduction
625(2)
18.2 Image-Guided Surgery
627(3)
18.2.1 Overview of IGS Applications
627(1)
18.2.2 Medical Augmented Reality
628(2)
18.3 Registration
630(4)
18.3.1 Tissue Deformation and Brain Shift
631(1)
18.3.2 Fiducial-Based Registration
631(2)
18.3.3 Point-Based Registration
633(1)
18.4 Calibration and Tracking
634(7)
18.4.1 Calibrating Instruments
634(3)
18.4.2 Camera Calibration
637(2)
18.4.3 Optical Tracking
639(1)
18.4.4 Electro-Magnetic Tracking
640(1)
18.4.5 Summary
641(1)
18.5 Navigated Control
641(1)
18.6 Display Modes
642(6)
18.6.1 Brief History of Medical AR
643(1)
18.6.2 Optical See-Through Displays
644(1)
18.6.3 Video See-Through Displays
645(1)
18.6.4 Augmented Microscope Displays
645(1)
18.6.5 Augmented Reality Windows
646(1)
18.6.6 Projection-Based Medical Augmented Reality
647(1)
18.7 Visualization Techniques for Medical Augmented Reality
648(9)
18.7.1 The Occlusion Problem of Augmented Reality
648(1)
18.7.2 Depth Cues in Augmented Reality
649(1)
18.7.3 Basic Visualization in AR
650(1)
18.7.4 Smart Visibility in AR
651(2)
18.7.5 Illustrative Visualization in AR
653(1)
18.7.6 Interaction in the OR
654(3)
18.7.7 Calibrated Augmented Reality Endoscope
657(1)
18.8 Applications
657(4)
18.8.1 Workflow Analysis for Medical Augmented Reality
657(2)
18.8.2 Neurosurgery
659(1)
18.8.3 Liver Surgery
659(2)
18.8.4 Validation and Clinical Evaluation
661(1)
18.9 Summary
661(4)
19 Visual Exploration Of Simulated And Measured Flow Data
665(50)
19.1 Introduction
665(1)
19.2 Basic Flow Visualization Techniques
666(9)
19.2.1 Direct Flow Visualization Techniques
666(1)
19.2.2 Feature-Based Flow Visualization Techniques
667(3)
19.2.3 Texture-Based Flow Visualization
670(1)
19.2.4 Geometry-Based Flow Visualization Methods
670(3)
19.2.5 Partition-Based Flow Visualization Techniques
673(1)
19.2.6 Evaluation of Flow Visualization Techniques
674(1)
19.3 From Medical Image Data to Simulation Models
675(9)
19.3.1 Segmentation and Meshing for Simulation
675(1)
19.3.2 Requirements for Surface Meshes
676(2)
19.3.3 Generation of Surface Meshes
678(2)
19.3.4 Generation of Volume Grids
680(4)
19.4 Visual Exploration of Measured Cardiac Blood Flow
684(8)
19.4.1 Medical Background
684(1)
19.4.2 Image Acquisition
685(2)
19.4.3 Preprocessing Cardiac Blood Flow Data
687(1)
19.4.4 Quantitative Analysis
688(1)
19.4.5 Visual Exploration
689(1)
19.4.6 Illustrative Visualization Techniques
690(1)
19.4.7 Uncertainty Visualization
691(1)
19.5 Exploration of Simulated Cerebral Blood Flow
692(15)
19.5.1 Blood Flow Simulations
693(2)
19.5.2 Extraction of Landmarks
695(2)
19.5.3 Anatomy-Guided Flow Exploration
697(3)
19.5.4 Lens-Based Interaction
700(1)
19.5.5 Visualization of Vasculature and Embedded Flow
701(1)
19.5.6 Virtual Stenting
702(2)
19.5.7 Software Assistant
704(2)
19.5.8 Validation
706(1)
19.5.9 Discussion
706(1)
19.6 Biomedical Simulation and Modeling
707(5)
19.6.1 Biomechanical Simulation in Orthopedics
707(2)
19.6.2 Simulation and Visualization for Planning Radio-Frequency Ablation
709(3)
19.7 Concluding Remarks
712(3)
20 Visual Computing For Ent Surgery Planning (Online
Chapter)
715(2)
21 Computer-Assisted Medical Education (Online
Chapter)
717(2)
22 Outlook (Online
Chapter)
719(2)
References 721(80)
Index 801
Bernhard Preim was born in 1969 in Magdeburg, Germany. He received the diploma in computer science in 1994 (minor in mathematics) and a Ph.D. in 1998 for a thesis on interactive visualization for anatomy education from the Otto-von-Guericke University of Magdeburg. In 1999 he moved to Bremen where he joined the staff of MEVIS and directed the computer-aided planning in liver surgery” group. Since Mars 2003 he is full professor for Visualization at the computer science department at the Otto-von-Guericke-University of Magdeburg, heading a research group focussed on medical visualization. His research interests include vessel visualization, exploration of blood flow, visual analytics in public health, virtual reality in medical education and since recently narrative visualization. He authored Visualization in Medicine” (Co-author Dirk Bartz, 2007) and Visual Computing in Medicine” (Co-author: C. Botha, 2013). Bernhard Preim founded the working group Medical Visualization in the German Society for Computer Science and served as speaker from 2003-2012. He was president of the German Society for Computer- and Robot-Assisted Surgery (www.curac.org). He was Co-Chair and Co-Organizer of the first and second Eurographics Workshop on Visual Computing in Biology and Medicine (VCBM) in 2008 and 2010 and lead the steering committee of that workshop until 2019. He is the chair of the scientific advisory board of ICCAS (International Competence Center on Computer-Assisted Surgery Leipzig, since 2010). From 2011-2018 he was an associate editor of IEEE Transactions on Medical Imaging and and IEEE Transactions on Visualization and Graphics (2017-2022). Currently he serves in the editorial board of Computers & Graphics (since 2019). He was also regularly a Visiting Professor at the University of Bremen where he closely collaborates with Fraunhofer MEVIS (2003-2012) and was Visiting Professor at TU Vienna (2016). Charl Botha is Professor of Visualisation at the Delft University of Technology (TU Delft) in the Netherlands, where he directs the medical visualisation lab. His research focuses on surgical planning and guidance, and visual analysis for medical research. He has published on, amongst other topics, virtual colonoscopy, shoulder replacement, diffusion tensor imaging and the visual analysis of human motion. Together with Bernhard Preim he initiated the Eurographics Workshop series on Visual Computing for Biology and Medicine, acted as co-chair in 2008 and 2010, and is currently serving as editor together with Prof. Preim of the Computers and Graphics special issue on VCBM.Prior to his Ph.D. he worked in industry designing embedded image processing systems and algorithms for two different companies.
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