Medical Image Analysis

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