Frangi A.F. Ed. creator Prince J.I. Sonka M. Ed. Academic Press 2024 eng Eng lis h 658 Table of contents PART I Introductory topics 1. Medical imaging modalities Mathias Unberath and Andreas Maier 1.1 Introduction 1.2 Image quality 1.3 Modalities and contrast mechanisms 1.4 Clinical scenarios 1.5 Exercises References 2. Mathematical preliminaries Carlos Alberola-López and Alejandro F. Frangi 2.1 Introduction 2.2 Imaging: definitions, quality and similarity measures 2.3 Vector and matrix theory results 2.4 Linear processing and transformed domains 2.5 Calculus 2.6 Notions on shapes 2.7 Exercises References 3. Regression and classification Thomas Moreau and Demian Wassermann 3.1 Introduction Nomenclature 3.2 Multidimensional linear regression 3.3 Treating non-linear problems with linear models 3.4 Exercises References 4. Estimation and inference Gonzalo Vegas Sánchez-Ferrero and Carlos Alberola-López 4.1 Introduction: what is estimation? 4.2 Sampling distributions 4.3 Estimation. Data-based methods 4.4 A working example 4.5 Estimation. Bayesian methods 4.6 Monte Carlo methods 4.7 Exercises References PART II Image representation and processing 5. Image representation and 2D signal processing Santiago Aja-Fernández, Gabriel Ramos-Llordén, and Paul A. Yushkevich 5.1 Image representation 5.2 Images as 2D signals 5.3 Frequency representation of 2D signals 5.4 Image sampling 5.5 Image interpolation 5.6 Image quantization 5.7 Further reading 5.8 Exercises References 6. Image filtering: enhancement and restoration Santiago Aja-Fernández, Ariel H. Curiale, and Jerry L. Prince 6.1 Medical imaging filtering 6.2 Point-to-point operations 6.3 Spatial operations 6.4 Operations in the transformdomain 6.5 Model-based filtering: image restoration 6.6 Further reading 6.7 Exercises References 7. Multiscale and multiresolution analysis Jon Sporring 7.1 Introduction 7.2 The image pyramid 7.3 The Gaussian scale-space 7.4 Properties of the Gaussian scale-space 7.5 Scale selection 7.6 The scale-space histogram 7.7 Exercises References PART III Medical image segmentation 8. Statistical shape models Tim Cootes 8.1 Introduction 8.2 Representing structures with points 8.3 Comparing shapes 8.4 Aligning two shapes 8.5 Aligning a set of shapes 8.6 Building shape models 8.7 Statistical models of texture 8.8 Combined models of appearance (shape and texture) 8.9 Image search 8.10 Exhaustive search 8.11 Alternating approaches 8.12 Constrained local models 8.13 3D models 8.14 Recapitulation 8.15 Exercises References 9. Segmentation by deformable models Jerry L. Prince 9.1 Introduction 9.2 Boundary evolution 9.3 Forces and speed functions 9.4 Numerical implementation 9.5 Other considerations 9.6 Recapitulation 9.7 Exercises References 10. Graph cut-based segmentation Jens Petersen, Ipek Oguz, and Marleen de Bruijne 10.1 Introduction 10.2 Graph theory 10.3 Modeling image segmentation using Markov random fields 10.4 Energy function, image term, and regularization term 10.5 Graph optimization and necessary conditions 10.6 Interactive segmentation 10.7 More than two labels 10.8 Recapitulation 10.9 Exercises References PART IV Medical image registration 11. Points and surface registration Shan Cong and Li Shen 11.1 Introduction 11.2 Points registration 11.3 Surface registration 11.4 Summary 11.5 Exercises References 12. Graph matching and registration Aristeidis Sotiras, Mattias Heinrich, Julia Schnabel, and Nikos Paragios 12.1 Introduction 12.2 Graph-based image registration 12.3 Exercises References 13. Parametric volumetric registration Paul A. Yushkevich, Miaomiao Zhang, and Jon Sporring 13.1 Introduction to volumetric registration 13.2 Mathematical concepts 13.3 Parametric volumetric registration 13.4 Exercises References 14. Non-parametric volumetric registration Paul A. Yushkevich and Miaomiao Zhang 14.1 Introduction 14.2 Mathematical concepts 14.3 Optical flow and related non-parametric methods 14.4 Large deformation diffeomorphic metric mapping 14.5 Exercises References 15. Image mosaicking Sophia Bano and Danail Stoyanov 15.1 Introduction 15.2 Motion models 15.3 Matching 15.4 Clinical applications 15.5 Recapitulation 15.6 Exercises References PART V Machine learning in medical image analysis 16. Deep learning fundamentals Nishant Ravikumar, Arezoo Zakeri, Yan Xia, and Alejandro F. Frangi 16.1 Introduction 16.2 Learning as optimization 16.3 Inductive bias, invariance, and equivariance 16.4 Recapitulation 16.5 Further reading 16.6 Exercises References 17. Deep learning for vision and representation learning Arezoo Zakeri, Yan Xia, Nishant Ravikumar, and Alejandro F. Frangi 17.1 Introduction 17.2 Convolutional neural networks 17.3 Deep representation learning 17.4 Recapitulation 17.5 Further reading 17.6 Exercises References 18. Deep learning medical image segmentation Sean Mullan, Lichun Zhang, Honghai Zhang, and Milan Sonka 18.1 Introduction 18.2 Convolution-based deep learning segmentation 18.3 Transformer-based deep learning segmentation 18.4 Hybrid deep learning segmentation 18.5 Training efficiency 18.6 Explainability 18.7 Case study 18.8 Recapitulation 18.9 Further reading 18.10 Exercises References 19. Machine learning in image registration Bob D. de Vos, Hessam Sokooti, Marius Staring, and Ivana Išgum 19.1 Introduction 19.2 Image registration with deep learning 19.3 Deep neural network architecture 19.4 Supervised image registration 19.5 Unsupervised image registration 19.6 Recapitulation 19.7 Exercises References PART VI Advanced topics in medical image analysis 20. Motion and deformation recovery and analysis James S. Duncan and Lawrence H. Staib 20.1 Introduction 20.2 The unmet clinical need 20.3 Image-centric flow fields: Eulerian analysis 20.4 Object-centric, locally derived flow fields: Lagrangian Analysis 20.5 Multiframe analysis: Kalman filters, particle tracking 20.6 Advanced strategies: model-based analysis and data-driven deep learning 20.7 Evaluation 20.8 Recapitulation 20.9 Exercises References 21. Imaging Genetics Marco Lorenzi and Andre Altmann 21.1 Introduction 21.2 Genome-wide association studies 21.3 Multivariate approaches to imaging genetics 21.4 Exercises References PART VII Large-scale databases 22. Detection and quantitative enumeration of objects from large images Cheng Lu, Simon Graham, Nasir Rajpoot, and Anant Madabhushi 22.1 Introduction 22.2 Classical image analysis methods 22.3 Learning from data 22.4 Detection and counting of mitotic cells using Bayesian modeling and classical image processing 22.5 Detection and counting of nuclei using deep learning 22.6 Recapitulation 22.7 Exercises References 23. Image retrieval in big image data Sailesh Conjeti, Stefanie Demirci, and Vincent Christlein 23.1 Introduction 23.2 Global image descriptors for image retrieval 23.3 Deep learning-based image retrieval 23.4 Efficient indexing strategies 23.5 Exercises References PART VIII Evaluation in medical image analysis 24. Assessment of image computing methods Ipek Oguz, Melissa Martin, and Russell T. Shinohara 24.1 The fundamental methodological concept 24.2 Introduction 24.3 Evaluation for classification tasks 24.4 Learning and validation 24.5 Evaluation for segmentation tasks 24.6 Evaluation of registration tasks 24.7 Intra-rater and inter-rater comparisons 24.8 Recapitulation 24.9 Exercises References 9780128136577 251127 20251217120318.0