Insight into images: principles and practice for segmentation, registration, and image analysis

Terry S. Yoo9781568812175, 1568812175

An introduction to the theory of modern medical image processing, including the analysis of data from – X-ray computer tomography, – magnetic resonance imaging, – nuclear medicine, – and ultrasound.
Using an algorithmic approach, and providing the mathematical, statistical, or signal processing as needed for background, the authors describe the principles of all methods implemented in the Insight Toolkit (ITK), a freely available, open- source, object-oriented library. The emphasis is on providing intuitive descriptions of the principles and illustrative examples of results from the leading filtering, segmentation, and registration methods.
This text covers the mathematical foundations of important techniques such as: – Statistical pattern recognition, – PDE-based nonlinear image filtering, – Markov random fields, – Level set methods, – Deformable models, – Mutual information, image-based registration – Non-rigid image data fusion
With contributions from: Elsa Angelini, Brian Avants, Stephen Aylward, Ting Chen, Jeffrey Duda, Jim Gee, Luis Ibanez, Celina Imielinska, Yinpeng Jin, Jisung Kim, Bill Lorensen, Dimitris Metaxas, Lydia Ng, Punam Saha, George Stetten, Tessa Sundaram, Jay Udupa, Ross Whitaker, Terry Yoo, and Ying Zhuge.
The Insight Toolkit is part of the Visible Human Project from the National Library of Medicine, with support from NIDCR, NINDS, NIMH, NEI, NSF, TATRC, NCI, and NIDCD.

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Table of contents :
Front cover……Page 1
Title page……Page 3
Date-line……Page 4
Dedications……Page 5
Insight Software Consortium……Page 7
Contents……Page 9
Foreword……Page 13
I Introduction and Basics……Page 19
1 Introduction……Page 21
1.1 Medical Image Processing……Page 22
1.2 A Brief Retrospective on 3D Medical Imaging……Page 23
1.3 Medical Imaging Technology……Page 24
1.4 Acquisition, Analysis, and Visualization……Page 34
1.5 Summary……Page 35
2.1 Introduction……Page 37
2.2 Images……Page 38
2.3 Point Operators……Page 39
2.4 Linear Filtering……Page 43
2.5 The Fourier Transform……Page 55
2.6 Summary……Page 63
3.1 Introduction……Page 65
3.2 Background……Page 67
3.3 Quantitative Comparison of Classifiers……Page 74
3.4 Classification Systems……Page 77
3.5 Summary of Classifiers’Performance……Page 102
3.6 Goodness-of-Fit……Page 105
3.7 Conclusion……Page 110
3.8 Appendix: Extruded Gaussian Distributions……Page 111
4.2 Gaussian Blurring and the Heat Equation……Page 121
4.3 Numerical Implementations……Page 128
II Segmentation……Page 137
5.1 Introduction……Page 139
5.2 Statistical Pattern Recognition……Page 141
5.3 Region Growing……Page 142
5.4 Active Surfaces/Front Evolution……Page 144
5.5 Combining Segmentation Techniques……Page 145
5.6 Looking Ahead……Page 146
6.1 Background……Page 149
6.2 Outline of the Chapter……Page 151
6.3 Basic Notations and Definitions……Page 154
6.4 Theory……Page 156
6.5 Methods and Algorithms……Page 169
6.6 Applications……Page 179
6.7 Concluding Remarks……Page 189
7.1 Markov Random Field Models: Introduction and Previous Work……Page 199
7.2 Gibbs Prior Model Theories……Page 200
7.3 Bayesian Framework and Posterior Energy Function……Page 203
7.5 Experiments and Results……Page 204
8.1 Introduction……Page 211
8.2 Deformable Surfaces……Page 213
8.3 Numerical Methods……Page 217
8.4 Applications……Page 226
8.5 Summary……Page 232
9.1 Introduction……Page 237
9.2 Previous Work……Page 238
9.3 Deformable Model Theories……Page 240
9.4 Experiments, Results, and Applications in ITK……Page 248
III Registration……Page 255
10.1 Introduction……Page 257
10.2 Image Registration Concepts……Page 258
10.3 A Generic Software Framework for Image to Image Registration……Page 260
10.4 Examples……Page 313
11.1 Introduction……Page 325
11.2 Optical Flow: Fast Mono-Modality Non-Rigid Registration……Page 330
11.3. Variational Framework for Computational Anatomy……Page 339
11.4 Review……Page 356
IV Hybrid Methods – Mixed Approaches to Segmentation……Page 367
12.1 Introduction……Page 369
12.2 Review of Segmentation Methods……Page 371
12.3 Hybrid Segmentation Engine……Page 375
12.4 Hybrid Segmentation: Integration of FC,VD, and DM……Page 376
12.5 Evaluation of Segmentation……Page 383
12.6 Results……Page 385
12.7 Conclusions……Page 393
Index……Page 407
Plate III……Page 412
Plate V……Page 413
Plate VIII……Page 414
Plate X……Page 415
Plate XIII……Page 416
Plate XV……Page 417
Plate XVII……Page 418
Plate XIX……Page 419
Back cover……Page 420