Statistical Shape and Deformation Analysis
Methods, Implementation and Applications
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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.
Key Features
- Presents an accessible introduction to the basic concepts, methods, algorithms and techniques in statistical shape and deformation analysis
- Includes implementation examples using open source software
- Covers real-life applications of statistical shape and deformation analysis methods
Readership
Researchers in medical imaging, computer scientists working in computer vision, electronic and biomedical engineers. Graduate students in these fields taking a course in Statistical Shape and Deformation Analysis
Table of Contents
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
Product details
- No. of pages: 508
- Language: English
- Copyright: © Academic Press 2017
- Published: March 23, 2017
- Imprint: Academic Press
- eBook ISBN: 9780128104941
- Paperback ISBN: 9780128104934
About the Authors
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
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