Contents -- Preface -- Chapter 1 Geometrical and Structural Analysis of Vessel Systems in 3D Medical Image Datasets Tobias Boskamp, Horst K. Hahn, Milo Hindennach, Stephan Zidowitz, Heinz-Otto Peitgen, Steffen Oeltze and Bernhard Preim -- 1. Introduction -- 2. Methods -- 2.1. Pre-processing -- 2.2. Segmentation -- 2.2.1. Progressive region growing -- 2.3. Skeletonization -- 2.3.1. Basic properties -- 2.3.2. Skeletonization by discrete distance transform -- 2.3.3. Continous skeletonization -- 2.3.4. Skeletonization by erosion -- 2.3.5. Distance ordered homotopic thinning -- 2.4. Topological analysis -- 2.4.1. Graph generation -- 2.4.2. Separation of vessel systems -- 2.4.3. Matching of vessel systems -- 2.5. Visualization of vascular structures -- 2.5.1. Visualization of vessels -- 2.5.2. Visualization with truncated cones -- 2.5.3. Implicit surface models -- 2.5.4. Blending -- 2.5.5. Visualization of vasculature with convolution surfaces -- 2.5.6. Exploration of vasculature -- 2.5.7. Evaluation and validation -- 2.5.8. Quantitative validation -- 2.5.9. Evaluation -- 2.5.10. Results -- 3. Applications -- 3.1. Morphometric quantification and visualization of vessels -- 3.1.1. Segmentation -- 3.1.2. Lumen boundary identification -- 3.1.3. Morphometric analysis of vessel cross-sections -- 3.1.4. Evaluation -- 3.2. Computer assisted diagnosis of cerebral aneurysms -- 3.2.1. Anatomical adaptive segmentation -- 3.2.2. Aneurysm classification -- 3.2.3. Clinical application -- 3.3. Analysis of vasculature for liver surgery planning -- 3.3.1. Image analysis -- 3.3.2. Approximation of liver territories -- 3.3.3. Surgery planning -- 3.4. Simulation of vascular systems -- 3.4.1. Related work -- 3.4.2. Global constructive vascular optimization -- 3.4.3. Discussion and perspectives -- 4. Conclusion -- Acknowledgments -- References.

Chapter 2 Automatic Methods to Analyze and Quantify Cardiac Left Ventricle Parameters by Means of SPECT Marco A. Gutierrez, Sergio S. Furuie, Marina S. Rebelo and Jose C. Meneghetti -- 1. Introduction -- 2. Automatic Analysis of Cardiac Nuclear Images -- 2.1. Planar imaging analysis -- 2.2. SPECT imaging analysis -- 2.3. Gated SPECT imaging analysis -- 3. Motion Analysis in SPECT -- 3.1. Computing velocity vector field -- 3.1.1. Gradient-based approach -- 3.1.2. Frequency-based approach -- 3.1.3. Spatio-temporal frequency-based approach -- 3.2. Kinetic energy estimation -- 3.3. Experimental evaluation -- 3.3.1. Numerical phantom simulations -- 3.3.2. Real images -- 4. Conclusions and Outlook -- Acknowledgments -- References -- Chapter 3 Left Ventricular Boundary Segmentation from Echocardiography Ning Lin, Weichuan Yu and James S. Duncan -- 1. Introduction -- 2. Combinative Multi-Scale Level Set Framework for Echocardiographic Image Segmentation -- 2.1. Background -- 2.2. Combinative multi-scale level set framework -- 2.2.1. Intensity distribution and boundary shape in Gaussian pyramid -- 2.2.2. Level set methods at different scale levels -- 2.3. Experiment -- 2.3.1. Comparison of Gaussian model fits at different scale levels -- 2.3.2. Segmentation results -- 2.4. Conclusion and future work -- Acknowledgments -- References -- Chapter 4 Application of Ultrasound-Based Computational Fluid Dynamics to Modeling Blood Flow in the Carotid Bifurcation Alexander D. Augst, Ben Ariff, Dean C. Barratt, Simon A. MCG. Thom, Alun D. Hughes and X. Yun Xu -- 1. Introduction -- 1.1. Arterial structure and disease -- 1.2. Necessity for realistic numerical models -- 2. Background -- 2.1. Idealized CFD models -- 2.2. Medical imaging techniques -- 2.2.1. 3D methods -- 2.2.2. Doppler flow measurements -- 2.3. Image-based CFD -- 3. Methodology -- 3.1. Introduction.

3.2. 3D ultrasound image acquisition -- 3.3. Image segmentation -- 3.4. Reconstruction -- 3.4.1. Creation of surface splines using MATLAB -- 3.4.2. Reconstruction of 3D surfaces in ICEM-DDN -- 3.5. Mesh generation -- 3.6. CFX-4.4 code -- 3.6.1. Governing equations -- 3.6.2. Discretization -- 3.6.3. Boundary conditions -- 3.7. Implementation of realistic boundary conditions -- 3.8. The non-Newtonian model -- 3.9. Visualization -- 3.1. Introduction -- 3.2. 3D ultrasound image acquisition -- 3.3. Image segmentation -- 4. Validation -- 4.1. Computational details -- 4.2. Phantom validation -- 5. Development of In Vivo Protocol -- 5.1. Evaluation of Doppler with MRI -- 5.2. Assessment of outlet flow and geometric limitations -- 5.2.1. Outlet flow boundaries -- 5.2.2. Geometric limitations -- 5.3. Implications for clinical studies -- 5.4. Preliminary reproducibility study -- 6. Application of the Methodology to a Sample Subject -- 7. Conclusion -- References -- Chapter 5 PDEs-Based Segmentation of Real-Time Three-Dimensional Echocardiography: Theory and Applications Alessandro Sarti, Cristiana Corsi and Claudio Lamberti -- 1. Introduction -- 1.1. Echography for cardiac examination -- 1.2. Segmentation by curve evolution -- 2. Acquisition System -- 2.1. Volumetrics -- 2.2. Sonos 7500 -- 3. Methods for LV Volumes Estimation -- 3.1. Level set models -- 3.1.1. The basic approach -- 3.1.2. Level set methods for image analysis -- 3.1.3. Numerical implementation -- 3.1.4. Results -- 3.2. Subjective surfaces -- 3.2.1. The model -- 3.2.2. Numerical implementation -- References -- Chapter 6 Logic Characterization and Classi.cation of ECG signals Adam Gacek and Witold Pedrycz -- 1. Introduction -- 2. Problem Statement -- 3. The Architecture -- 4. The Refinement of the Architecture: A Cascade Structure.

5. The Design Aspects of the Networks: An Evolutionary Optimization -- 6. The Classification Facets of the Network -- 7. Experimental Studies -- 7.1. Synthetic data -- 7.2. QRS complexes -- 8. Concluding Comments -- References -- Chapter 7 Techniques in Image Segmentation and 3D Visualization in Brain MRI and their Applications Hyunwook Park, Min Jeong Kwon and Yeji Han -- 1. Introduction -- 2. Segmentation Methods -- 2.1. Region detection methods -- 2.1.1. Thresholding -- 2.1.2. Region-based approaches -- 2.1.3. Statistical approaches -- 2.1.4. Clustering -- 2.1.5. Arti.cial neural networks -- 2.2. Boundary detection methods -- 2.2.1. Parametric deformable models -- 2.2.2. Geometric deformable models -- 2.3. An example of segmentation of brain MR images -- 2.4. Difficulties of segmentation in brain MR images -- 3. 3D Visualization -- 3.1. Visualization of conventional images -- 3.1.1. Surface rendering -- 3.1.2. Volume rendering -- 3.1.3. Application of 3D visualization of anatomical datasets -- 3.2. Visualization of functional MRI and diffusion tensor images -- 3.2.1. Functional MRI -- 3.2.2. Visualization of diffusion tensor MRI -- 4. Conclusion -- References -- Chapter 8 Techniques in Analyzing the Neocortical Fine-Structure F. Kruggel -- 1. Introduction -- 2. Materials and Methods -- 2.1. Brain preparation and scanning -- 2.2. Preprocessing -- 2.3. White matter segmentation -- 2.4. Segmentation of basins -- 2.5. Computation of the interfaces -- 2.6. Determination of the neo-cortical intensity profiles -- 2.7. Classification of the neo-cortical fine-structure -- 3. Experiments -- 3.1. Evaluation -- 3.2. Comparison of MR images and myelin-stained slices -- 3.3. Visual cortex (Area 17) -- 3.4. Motor and sensory cortex (Area 4 and 3) -- 3.5. Broca's area (Area 44 and 45) -- 4. Discussion -- Acknowledgments -- References.

Chapter 9 Techniques in Automatic Cortical Gray Matter Segmentation of Three-Dimensional (3D) Brain Images Roman Goldenberg, Ron Kimmel, Ehud Rivlin and Michael Rudzsky -- 1. Introduction -- 2. Statistical Methods -- 2.1. Inhomogeneity correction -- 2.2. Markov random fields -- 3. Fuzzy Segmentation -- 3.1. Fuzzy clustering methods -- 3.2. Fuzzy Markovian segmentation -- 4. Deformable Models -- 5. Summary -- References -- Chapter 10 Computer Techniques for the Automatic Segmentation of 3D MR Brain Images Alan Wee-Chung Liew and Hong Yan -- 1. Introduction -- 2. Magnetic Resonance Imaging -- 3. MR Image Artifacts -- 4. The MRI Segmentation Problem -- 5. A Review of MRI Segmentation Methods -- 5.1. Image preprocessing for INU correction -- 5.2. Classification-based segmentation -- 5.3. Region-based segmentation -- 5.4. Contour-based segmentation -- 5.5. Other approaches -- 6. A Novel FCM-Based Adaptive Segmentation Method -- 6.1. FCM clustering for soft segmentation -- 6.2. Incorporation of local spatial continuity -- 6.3. INU bias field compensation -- 6.4. The adaptive spatial FCM algorithm -- 6.5. Algorithm implementation -- 6.5.1. Slice-wise spline surface computation -- 6.5.2. Iterative update of spline coefficients -- 6.5.3. The complete algorithm -- 7. Experimental Results -- 7.1. INU compensation and segmentation -- 7.1.1. Visual evaluation -- 7.1.2. Quantitative evaluation -- 7.2. Partial volume estimation -- 7.3. Variation of algorithmic parameters -- 7.4. Segmentation performance on actual MRI data -- 8. Conclusions -- Acknowledgment -- References -- Chapter 11 Techniques in the Enhancement of 3D Angiograms and their Applications Karl Krissian and Gunnar Farneback -- 1. Introduction -- 1.1. Brief introduction to vascular imaging -- 1.2. Main visualization techniques -- 1.3. Image processing techniques -- 2. Extraction of the Orientation.

2.1. Notations.