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Statistical Shape and Deformation Analysis

Methods, Implementation and Applications

1st Edition - March 23, 2017

Authors: Guoyan Zheng, Shuo Li, Gabor Szekely

Paperback ISBN:

9 7 8 - 0 - 1 2 - 8 1 0 4 9 3 - 4

eBook ISBN:

9 7 8 - 0 - 1 2 - 8 1 0 4 9 4 - 1

Statistical Shape and Deformation Analysis: Methods, Implementation and Applications contributes enormously to solving different problems in patient care and physical… Read more

Image - Statistical Shape and Deformation Analysis

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Statistical Shape and Deformation Analysis: Methods, Implementation and Applications contributes enormously to solving different problems in patient care and physical anthropology, ranging from improved automatic registration and segmentation in medical image computing to the study of genetics, evolution and comparative form in physical anthropology and biology.

This book gives a clear description of the concepts, methods, algorithms and techniques developed over the last three decades that is followed by examples of their implementation using open source software.

Applications of statistical shape and deformation analysis are given for a wide variety of fields, including biometry, anthropology, medical image analysis and clinical practice.

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

Guoyan Zheng

Prof. Zheng is the Head of the Information Processing in Medical Imaging Group, the Institute for Surgical Technology and Biomechanics, University of Bern. In 2010, he did his habilitation was awarded the title ‘Privatdozent’ from the same university. His research interests include medical image computing, machine learning, computer assisted interventions, medical robotics, and multi-modality image analysis. He has published over 160 peer-reviewed journal and conference papers and was granted 6 US and European patents. He has won over ten national and international awards/prizes including the best basic science paper published in the Journal of Laryngology and Otology in year 2011, the 2009 Ypsomed Innovation Prize, and the best technical paper award in the 2006 annual conference of the International Society of Computer Assisted Orthopaedic Surgery. He is on the program committee of the 16th, the 18th and the 19th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI 2013, 2015 and 2016). He is the general chair of the 7th International Workshop on Medical Imaging and Augmented Reality (MIAR 2016).

Affiliations and expertise

University of Bern, Bern, Switzerland.

Shuo Li

Dr. Li is the Director of Digital Imaging Group (DIG) of London, an associate professor in the department of medical imaging and medical biophysics at the University of Western Ontario and a scientist at the Lawson Health Research Institute. Before this position, he was a research scientist and project manager in general electric (GE) healthcare for 9 years. He funds and directs the DIG (http://digitalimaginggroup.ca/) since 2006, which is a highly dynamic and multiple disciplinary group. He received his Ph.D. degree in computer science from Concordia University 2006, where his Ph.D. thesis won the doctoral prize giving to the most deserving graduating student in the faculty of engineering and computer science. He has published over 100 publications; He is the recipient of several GE internal awards, and he serves as guest editor and associate editor in several prestigious journals in the field. Dr. Li also serves as a program committee member in highly influential conferences; and he is the editor of six books. His current interest is development intelligent analytic tools to help physicians and hospital administrative to handle the big medical data, centered with medical images.

Affiliations and expertise

University of Western Ontario, London, ON, Canada

Gabor Szekely

Prof. Székely graduated from the Technical University of Budapest in chemical engineering in 1974 and from the Eötvös Loránd University of Budapest in Applied Mathematics in 1981. He obtained his Ph.D. in analytical chemistry in 1985 from the Technical University of Budapest. Between 1974 and 1986 he has been working at the Computer Department of the Institute of Isotopes of the Hungarian Academy of Sciences, since 1985 as the Head of the Department, focusing on automatic structure elucidation of organic compounds. Between 1986 and 1990 he developed computer support systems for the chemical and biomedical applications of magnetic resonance at Bruker Spectrospin. In 1991 he joined the Computer Vision Laboratory of the ETH Zurich as a senior researcher. In 2002 he was elected as Associate Professor at the Department of Information Technology and Electrical Engineering and founded the Medical Image Analysis and Visualization research group. He has been promoted to Full Professor in 2008. Between 2001 and 2013 he has been the Director of the Swiss National Center for Competence in Research on Computer Aided and Image Guided Medical Interventions. In 2007 he co-founded the spinoff company Virtamed, producing virtual reality based system for surgical training. His major research interest is developing algorithms and clinical systems for the optimal computer support of medical diagnosis, therapy, training and education.

Affiliations and expertise

Computer Vision Lab, ETHZ, Switzerland