Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis / Edition 1

by Terry S. Yoo

Overview

A companion to the Insight Toolkit 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 book 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.

Product Details

ISBN-13:	9781568812175
Publisher:	Taylor & Francis
Publication date:	08/16/2004
Edition description:	New Edition
Pages:	418
Product dimensions:	6.12(w) x 9.19(h) x (d)

Table of Contents

	Foreword	xi
I	Introduction and Basics	1
1	Introduction	3
1.1	Medical Image Processing	4
1.2	A Brief Retrospective on 3D Medical Imaging	5
1.3	Medical Imaging Technology	6
1.4	Acquisition, Analysis, and Visualization	16
1.5	Summary	17
2	Basic Image Processing and Linear Operators	19
2.1	Introduction	19
2.2	Images	20
2.3	Point Operators	21
2.4	Linear Filtering	25
2.5	The Fourier Transform	37
2.6	Summary	45
3	Statistics of Pattern Recognition	47
3.1	Introduction	47
3.2	Background	49
3.3	Quantitative Comparison of Classifiers	56
3.4	Classification Systems	59
3.5	Summary of Classifiers' Performance	84
3.6	Goodness-of-Fit	87
3.7	Conclusion	92
3.8	Appendix: Extruded Gaussian Distributions	93
4	Nonlinear Image Filtering with Partial Differential Equations	103
4.1	Introduction	103
4.2	Gaussian Blurring and the Heat Equation	103
4.3	Numerical Implementations	110
II	Segmentation	119
5	Segmentation Basics	121
5.1	Introduction	121
5.2	Statistical Pattern Recognition	123
5.3	Region Growing	124
5.4	Active Surfaces/Front Evolution	126
5.5	Combining Segmentation Techniques	127
5.6	Looking Ahead	128
6	Fuzzy Connectedness	131
6.1	Background	131
6.2	Outline of the Chapter	133
6.3	Basic Notations and Definitions	136
6.4	Theory	138
6.5	Methods and Algorithms	151
6.6	Applications	161
6.7	Concluding Remarks	171
7	Markov Random Field Models	181
7.1	Markov Random Field Models: Introduction and Previous Work	181
7.2	Gibbs Prior Model Theories	182
7.3	Bayesian Framework and Posterior Energy Function	185
7.4	Energy Minimization	186
7.5	Experiments and Results	186
8	Isosurfaces and Level Sets	193
8.1	Introduction	193
8.2	Deformable Surfaces	195
8.3	Numerical Methods	199
8.4	Applications	208
8.5	Summary	214
9	Deformable Models	219
9.1	Introduction	219
9.2	Previous Work	220
9.3	Deformable Model Theories	222
9.4	Experiments, Results, and Applications in ITK	230
III	Registration	237
10	Medical Image Registration: Concepts and Implementation	239
10.1	Introduction	239
10.2	Image Registration Concepts	240
10.3	A Generic Software Framework for Image to Image Registration	242
10.4	Examples	295
11	Non-Rigid Image Registration	307
11.1	Introduction	307
11.2	Optical Flow: Fast Mono-Modality Non-Rigid Registration	312
11.3	Variational Framework for Computational Anatomy	321
11.4	Review	338
IV	Hybrid Methods - Mixed Approaches to Segmentation	349
12	Hybrid Segmentation Methods	351
12.1	Introduction	351
12.2	Review of Segmentation Methods	353
12.3	Hybrid Segmentation Engine	357
12.4	Hybrid Segmentation: Integration of FC, VD, and DM	358
12.5	Evaluation of Segmentation	365
12.6	Results	367
12.7	Conclusions	375
	Index	389