CONTENTS -- Preface -- Chapter 1 Acoustical Signals of Biomechanical Systems E. Kaniusas -- 1. Introduction -- 2. Body Sounds - An Overview -- 2.1. Heart sounds -- 2.2. Lung sounds -- 2.3. Snoring sounds -- 3. Mutual Interrelations of Body Sounds -- 4. Transmission of Body Sounds -- 4.1. Propagation of sounds -- 4.1.1. General issues -- 4.1.2. Spreading of sounds -- 4.1.3. Frequency dependant propagation -- 4.2. Attenuation of sounds -- 4.2.1. Volume effects -- 4.2.2. Inhomogeneity effects -- 4.3. Coupling of sounds -- 5. Spatial Distribution of Body Sounds -- 6. Concluding Remarks -- Acknowledgments -- References -- Chapter 2 Modeling Techniques for Liver Tissue Properties and their Application in Surgical Treatment of Liver Cancer J.-M. Schwartz, D. Laurendeau, M. Denninger, D. Rancourt and C. Simo -- 1. Soft Tissue Modeling -- 1.1. Earliest models -- 1.2. Spring-mass models -- 1.3. Boundary element methods -- 1.4. Finite element methods -- 1.5. Linear elastic tensor-mass model -- 2. Non-linear Modeling -- 2.1. Non-linear finite element models -- 2.2. Non-linear extensions of the tensor-mass model -- 2.2.1. Principle -- 2.2.2. Measure of deformation -- 2.2.3. Integration of geometrical non-linearity -- 2.2.4. Integration of viscoelasticity -- 3. Implementation and Performance -- 3.1. Data structure -- 3.2. Algorithm -- 3.3. Computational speed -- 3.3.1. Load of di.erent mechanical models -- 3.3.2. Dependence on mesh size -- 3.3.3. Dynamic adaptation -- 3.4. Implementation of the model on a distributed computer architecture -- 3.4.1. Principle -- 3.4.2. Performance evaluation -- 3.4.3. Implementation strategies -- 3.5. Numerical stability -- 4. Experimental Measurements and Validation -- 4.1. Mechanical setup -- 4.2. Experimental results -- 4.3. Comparisons with simulation models -- 4.3.1. Physically non-linear model.

4.3.2. Full non-linear model -- 4.3.3. Limitations -- 5. Simulation of Topological Changes -- 5.1. Overview -- 5.2. Algorithm -- 5.3. Simulation approach -- 6. Conclusion -- Acknowledgments -- References -- Chapter 3 A Survey of Biomechanical Modeling of the Brain for Intra-Surgical Displacement Estimation and Medical Simulation M. A. Audette, M. Miga, J. Nemes, K. Chinzei and T. M. Peters -- 1. Introduction -- 2. Preliminaries -- 2.1. Biomechanics -- 2.1.1. Elastic solid models -- 2.1.2. Fluid models -- 2.2. Numerical estimation -- 2.2.1. Finite element modeling -- 2.2.2. Toward constitutively realistic surgical simulation: Multi-rate FE and other e.ciencies -- 2.2.3. Mass-spring and mass-tensor systems -- 2.3. Cutting models -- 3. Finite Element Modeling of the Brain -- 3.1. Solid brain models -- 3.1.1. Non-linear solid continua: Hyper-elastic and Viscoelastic solids -- 3.1.2. Impact response FE models -- 3.1.3. Early rheological studies and strain models -- 3.1.4. Rheological studies and FE models for medical applications -- 4. Biphasic Brain Models -- 4.1. Biomechanics of the cranial cavity featuring solid and fluid components -- 4.2. Solid-liquid continua: Poro-elastic and mixture continua, with related FE models -- 5. Summary -- References -- Chapter 4 Techniques and Applications of Robust Nonrigid Brain Registration O. Clatz, H. Delingette, N. Archip, I.-F. Talos, A. J. Golby, P. Black, R. Kikinis, F. A. Jolesz, N. Ayache and S. K. Warfield -- 1. Introduction -- 1.1. Image-guided neurosurgery -- 1.2. Nonrigid registration for image-guided surgery -- 1.2.1. Modeling the intraoperative deformation -- 1.2.2. Displacement-based nonrigid registration -- 2. Method -- 2.1. Preoperative MR image treatment -- 2.1.1. Segmentation -- 2.1.2. Rigid registration -- 2.1.3. Biomechanical model -- 2.1.4. Block selection.

2.1.5. Computation of the structure tensor -- 2.2. Block-matching algorithm -- 2.3. Formulation of the problem: approximation versus interpolation -- 2.3.1. Approximation -- 2.3.2. Interpolation -- 2.4. Robust gradual transformation estimate -- 2.4.1. Formulation -- 2.4.2. Parameter setting -- 2.4.3. Implementation issues and time constraint -- 3. Experiments -- 4. Conclusion -- References -- Chapter 5 Optical Imaging in Cerebral Hemodynamics and Pathophysiology: Techniques and Applications Q. Luo, S. Chen, P. Li and S. Zeng -- 1. Introduction -- 2. Theory -- 2.1. Spectroscopic imaging -- 2.1.1. Absorption spectrum of oxyhemoglobin and deoxyhemoglobin -- 2.1.2. Physical model for the spectroscopic data analysis -- 2.2. Laser speckle flowmetry -- 2.2.1. Introduction of laser speckle phenomenon -- 2.2.2. Laser speckle contrast analysis for full field blood flow mapping -- 3. Instrumentation -- 3.1. Multi-wavelength reflectance imaging -- 3.2. Laser speckle imaging -- 3.3. Combination of multi-wavelength reflectance imaging and laser speckle imaging -- 4. Applications -- 4.1. Spatiotemporal quantification of cerebral hemodynamic and metabolism change during functional activation -- 4.2. Cortical spreading depression -- 4.3. Focal cerebral ischemia -- Acknowledgments -- References -- Chapter 6 The Auditory Brainstem Implant H. Takahashi, M. Nakao and K. Kaga -- 1. Clinical Study -- 1.1. History and system description -- 1.2. Implantation -- 1.3. Rehabilitation -- 2. Animal Study -- 2.1. Overview -- 2.1.1. Safety viewpoint -- 2.1.2. Functional viewpoint -- 2.2. Animal model of ABI -- 2.2.1. Auditory cortex of rat -- 2.2.2. Auditory evoked potentials -- 2.2.3. Surface microelectrode array -- 2.2.4. Spike microelectrode array -- 2.2.5. Animal preparation -- 2.2.6. Recording and test stimuli -- 2.3. Physiological proof of ABI feasibility.

2.3.1. Tone-evoked potentials in the auditory cortex -- 2.3.2. Microstimulation of the cochlear nucleus -- 3. Discussion -- 3.1. Cortical mapping of auditory evoked potential -- 3.2. Functional microstimulation in the cochlear nucleus -- 3.2.1. Feasibility of ABI -- 3.2.2. Implications for developing future ABI -- 4. Summary -- References -- Chapter 7 Spectral Analysis Techniques in the Detection of Coronary Artery Stenosis E. D. Übeyli and ˙ I. Güler -- 1. Introduction -- 2. Data Acquisition from Coronary Arteries -- 2.1. Continuous wave Doppler -- 2.2. Pulsed wave Doppler -- 3. Spectral Analysis Techniques -- 3.1. Nonparametric methods -- 3.1.1. Periodogram method -- 3.1.2. Correlogram method -- 3.1.3. Blackman-Tukey method -- 3.1.4. Bartlett method -- 3.1.5. Welch method -- 3.2. Parametric methods -- 3.2.1. AR method -- 3.2.1.1. Yule-Walker method -- 3.2.1.2. Covariance method -- 3.2.1.3. Modified covariance method -- 3.2.1.4. Burg method -- 3.2.1.5. Least squares method -- 3.2.2. MA method -- 3.2.3. ARMA method -- 3.2.4. Selection of AR, MA, and ARMA model orders -- 3.3. Time-frequency methods -- 3.3.1. Short-time Fourier transform -- 3.3.2. Wigner-Ville distribution -- 3.3.3. Wavelet transform -- 3.4. Eigenvector methods -- 3.4.1. Pisarenko method -- 3.4.2. MUSIC method -- 3.4.3. Minimum-norm method -- 4. Medical Decision Support Systems -- 5. Feature Extraction/Selection -- 6. Review of Different Decision Support Systems -- 6.1. Multilayer perceptron neural networks -- 6.2. Combined neural network models -- 6.3. Mixture of experts -- 6.4. Modified mixture of experts -- 6.5. Probabilistic neural network -- 6.6. Recurrent neural networks -- 6.7. Support vector machine -- 7. Experiments for Implementation of Decision Support Systems -- 8. Measuring Performance of Decision Support Systems -- 9. Discussion and Analysis -- 10. Conclusion.

References -- Chapter 8 Techniques in the Contour Detection of Kidneys and their Applications M. Martin-Fernandez, L. Cordero-Grande, E. Munoz-Moreno and C. Alberola-Lopez -- 1. Introduction -- 2. Contour Operations -- 2.1. Continuous contours -- 2.2. Discrete contours -- 2.2.1. Definition -- 2.2.2. Interpolation and uniform sampling -- 2.2.3. Discrete derivatives -- 2.2.4. Perimeter and area -- 2.2.5. Center and inertia matrix -- 2.2.6. Affine transformations -- 2.2.7. Contour fitting -- 2.3. Contour homogenizations -- 2.4. Manual template adjustment -- 2.4.1. Procedure description -- 2.4.2. Technical details about the rotations -- 2.5. Discussion -- 3. Solution Based on Shape Priors -- 3.1. Shape modeling -- 3.2. Image information -- 3.3. The algorithm -- 3.4. Discussion -- 4. Solution Based on Active Contours and Markov Random Fields -- 4.1. Active contours -- 4.2. Markov random fields -- 4.3. State of the art -- 4.4. The model -- 4.4.1. Prior model of the deformation .eld -- 4.4.2. Likelihood function -- 4.4.3. Complete model -- 4.5. Implementation details -- 4.6. Validation -- 4.7. Experimental validation results -- 4.8. 3D extension -- 4.9. Unsupervised parameter estimation -- 4.10. 3D parameter estimation and validation -- 5. Conclusions -- References.