Statistical Shape and Deformation Analysis Methods Implementation and Applications 1st Edition by Guoyan Zheng, Shuo Li and Gabor Székely 9780128104941 0128104945

Part I: Basic Concepts, Methods and Algorithms

Chapter 1: Automated Image Interpretation Using Statistical Shape Models
Abstract
Acknowledgements
1.1. Introduction
1.2. Statistical Shape Analysis
1.3. Feature Point Detection Using Shape Model Matching
1.4. Fully Automated Image Analysis via Shape Model Matching
1.5. Automated Image Interpretation and Its Applications
1.6. Limitations of Statistical Shape Models for Image Interpretation
1.7. Conclusion
References

Chapter 2: Statistical Deformation Model: Theory and Methods
Abstract
2.1. Introduction
2.2. Deformation Representation
2.3. Statistical Approaches
2.4. General-Purpose Deformation Models
2.5. Biophysics-Based Deformation
References

Chapter 3: Correspondence Establishment in Statistical Shape Modeling: Optimization and Evaluation
Abstract
3.1. Introduction
3.2. PDM and Shape Correspondence
3.3. Landmark Sliding for Shape Correspondence
3.4. Groupwise Shape Correspondence
3.5. Performance Evaluation of Shape Correspondence
3.6. Experiments
3.7. Conclusions and 3D Shape Correspondence
References

Chapter 4: Landmark-Based Statistical Shape Representations
Abstract
4.1. Introduction
4.2. Landmark-Based Shape Representation
4.3. Shape-Based Landmark Detection
4.4. Conclusion
References

Chapter 5: Probabilistic Morphable Models
Abstract
5.1. Introduction
5.2. Methods
5.3. Applications and Results
5.4. Conclusion
References

Chapter 6: Object Statistics on Curved Manifolds
Abstract
Acknowledgements
6.1. Objectives of Object Statistics
6.2. Objects Live on Curved Manifolds
6.3. Statistical Analysis Background
6.4. Advanced Statistical Methods for Manifold Data
6.5. Correspondence
6.6. How to Compare Representations and Statistical Methods
6.7. Results of Classification, Hypothesis Testing, and Probability Distribution Estimation
6.8. Conclusions
References

Chapter 7: Shape Modeling Using Gaussian Process Morphable Models
Abstract
7.1. Introduction
7.2. Shape Modeling Using Gaussian Processes
7.3. Non-Rigid Registration Using Gaussian Process Priors
7.4. Case Study: Building a Statistical Shape Model of the Skull
7.5. Modeling and Analyzing Pathologies
7.6. Conclusion
Appendix 7.A.
References

Chapter 8: Bayesian Statistics in Computational Anatomy
Abstract
8.1. Introduction
8.2. Parametric Bayesian Statistics
8.3. Nonparametric Bayesian Statistics
8.4. Conclusions and Open Problems
References

Part II: Open Source Implementation Examples

Chapter 9: Morpho and Rvcg – Shape Analysis in R
Abstract
9.1. Introduction
9.2. Preliminaries and Installation
9.3. Landmark Based Shape Analysis with Morpho
9.4. Manipulations on Triangular Meshes Using Rvcg (and Morpho)
9.5. Beyond CRAN
9.6. Final Remarks
References

Chapter 10: ShapeWorks
Abstract
10.1. Introducing ShapeWorks
10.2. Particle-Based Modeling
10.3. PBM Extensions
10.4. ShapeWorks Software Implementation and Workflow
10.5. ShapeWorks in Biomedical Applications
10.6. Conclusions and Future Work
References

Part III: Applications

Chapter 11: Applications of Statistical Deformation Model
Abstract
11.1. Image-Guided Prostate Intervention
11.2. Whole Heart Segmentation
References

Chapter 12: Statistical Shape and Deformation Models Based 2D–3D Reconstruction
Abstract
12.1. Introduction
12.2. Statistical Shape Model Based 2D–3D Reconstruction and Its Application in THA
12.3. Statistical Deformation Model Based 2D–3D Reconstruction
12.4. Final Remarks
References

Chapter 13: Statistical Shape Analysis for Brain Structures
Abstract
Acknowledgments
13.1. Introduction
13.2. Surface Modeling and Registration
13.3. Statistical Inference on the Surface
13.4. An Example Application
13.5. Conclusions
References

Chapter 14: Statistical Respiratory Models for Motion Estimation
Abstract
Acknowledgments
14.1. Background
14.2. 4-Dimensional MR Imaging
14.3. Motion Model Building
14.4. Establishment of Correspondence
14.5. Statistical Motion Modeling
14.6. Bayesian Reconstruction from Sparse Data
14.7. Applications of Population-Based Statistical Motion Models to Motion Reconstruction
14.8. Reconstruction by Regression
14.9. Conclusion
References

Chapter 15: Statistical Shape and Appearance Models for Bone Quality Assessment
Abstract
15.1. Introduction
15.2. Fundamentals of Statistical Shape and Appearance Models
15.3. Approaches for Bone Quality Assessment
15.4. Discussion and Conclusion
References

Chapter 16: Statistical Shape Models of the Heart: Applications to Cardiac Imaging
Abstract
16.1. Introduction
16.2. The heart
16.3. Cardiac Imaging Techniques
16.4. Statistical Shape Models
16.5. Discussion
References

Index