Advanced Biomedical Image Analysis E-Book

Mark Haidekker

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 936

Veröffentlichungsjahr: 2011

Beschreibung

A comprehensive reference of cutting-edge advanced techniques for quantitative image processing and analysis Medical diagnostics and intervention, and biomedical research rely progressively on imaging techniques, namely, the ability to capture, store, analyze, and display images at the organ, tissue, cellular, and molecular level. These tasks are supported by increasingly powerful computer methods to process and analyze images. This text serves as an authoritative resource and self-study guide explaining sophisticated techniques of quantitative image analysis, with a focus on biomedical applications. It offers both theory and practical examples for immediate application of the topics as well as for in-depth study. Advanced Biomedical Image Analysis presents methods in the four major areas of image processing: image enhancement and restoration, image segmentation, image quantification and classification, and image visualization. In each instance, the theory, mathematical foundation, and basic description of an image processing operator is provided, as well as a discussion of performance features, advantages, and limitations. Key algorithms are provided in pseudo-code to help with implementation, and biomedical examples are included in each chapter. Image registration, storage, transport, and compression are also covered, and there is a review of image analysis and visualization software. Members of the academic community involved in image-related research as well as members of the professional R&D sector will rely on this volume. It is also well suited as a textbook for graduate-level image processing classes in the computer science and engineering fields.

Leseprobe

CONTENTS

Cover

Half Title page

Title page

Copyright page

Preface

Chapter 1: Image Analysis: A Perspective

1.1 Main Biomedical Imaging Modalities

1.2 Biomedical Image Analysis

1.3 Current Trends in Biomedical Imaging

1.4 About This Book

References

Chapter 2: Survey of Fundamental Image Processing Operators

2.1 Statistical Image Description

2.2 Brightness and Contrast Manipulation

2.3 Image Enhancement and Restoration

2.4 Intensity-Based Segmentation (Thresholding)

2.5 Multidimensional Thresholding

2.6 Image Calculations

2.7 Binary Image Processing

2.8 Biomedical Examples

References

Chapter 3: Image Processing in the Frequency Domain

3.1 The Fourier Transform

3.2 Fourier-Based Filtering

3.3 Other Integral Transforms: The Discrete Cosine Transform and the Hartley Transform

3.4 Biomedical Examples

References

Chapter 4: Thewavelet Transform and Wavelet-Based Filtering

4.1 One-Dimensional Discrete Wavelet Transform

4.2 Two-Dimensional Discrete Wavelet Transform

4.3 Wavelet-Based Filtering

4.4 Comparison Of Frequency-Domain Analysis to Wavelet Analysis

4.5 Biomedical Examples

References

Chapter 5: Adaptive Filtering

5.1 Adaptive Noise Reduction

5.2 Adaptive Filters in the Frequency Domain: Adaptive Wiener Filters

5.3 Segmentation with Local Adaptive Thresholds and Related Methods

5.4 Biomedical Examples

References

Chapter 6: Deformable Models and Active Contours

6.1 Two-Dimensional Active Contours (Snakes)

6.2 Three-Dimensional Active Contours

6.3 Live-Wire Techniques

6.4 Biomedical Examples

References

Chapter 7: The Hough Transform

7.1 Detecting Lines and Edges with the Hough Transform

7.2 Detection of Circles and Ellipses with the Hough Transform

7.3 Generalized Hough Transform

7.4 Randomized Hough Transform

7.5 Biomedical Examples

References

Chapter 8: Texture Analysis

8.1 Statistical Texture Classification

8.2 Texture Classification with Local Neighborhood Methods

8.3 Frequency-Domain Methods for Texture Classification

8.4 Run Lengths

8.5 Other Classification Methods

8.6 Biomedical Examples

References

Chapter 9: Shape Analysis

9.1 Cluster Labeling

9.2 Spatial-Domain Shape Metrics

9.3 Statistical Moment Invariants

9.4 Chain Codes

9.5 Fourier Descriptors

9.6 Topological Analysis

9.7 Biomedical Examples

References

Chapter 10: Fractal Approaches to Image Analysis

10.1 Self-Similarity and the Fractal Dimension

10.2 Estimation Techniques for the Fractal Dimension in Binary Images

10.3 Estimation Techniques for the Fractal Dimension in Gray-Scale Images

10.4 Fractal Dimension in the Frequency Domain

10.5 Local Hölder Exponent

10.6 Biomedical Examples

References

Chapter 11: Image Registration

11.1 Linear Spatial Transformations

11.2 Nonlinear Transformations

11.3 Registration Quality Metrics

11.4 Interpolation Methods for Image Registration

11.5 Biomedical Examples

References

Chapter 12: Image Storage, Transport, and Compression

12.1 Image Archiving, Dicom, and Pacs

12.2 Lossless Image Compression

12.3 Lossy Image Compression

12.4 Biomedical Examples

References

Chapter 13: Image Visualization

13.1 Gray-Scale Image Visualization

13.2 Color Representation of Gray-Scale Images

13.3 Contour Lines

13.4 Surface Rendering

13.5 Volume Visualization

13.6 Interactive Three-Dimensional Rendering and Animation

13.7 Biomedical Examples

References

Chapter 14: Image Analysis and Visualization Software

14.1 Image Processing Software: An Overview

14.2 Imagej

14.3 Examples of Image Processing Programs

14.4 Crystal Image

14.5 OpenDX

14.6 Wavelet-Related Software

14.7 Algorithm Implementation

References

Appendix A: Design and Test of Simulations

Appendix B: Parallel Discrete-Event Simulation

Plates

Index

ADVANCED BIOMEDICAL IMAGE ANALYSIS

Copyright © 2011 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Haidekker, Mark A.

Advanced biomedical image analysis / Mark A. Haidekker

ISBN 978-0-470-62458-6

PREFACE

Medical imaging is one of the great revolutions in medicine. Traditionally, explorative surgery had to be performed to look inside a patient’s body, even to perform a diagnosis. Slightly more than a century ago, x-rays were discovered. With it came the ability to look inside the body without surgery. X-ray imaging was rapidly adopted in medical centers worldwide. A new medical subject area was created—radiology. For decades, the radiologist had a basic set of tools, the x-ray tube, a fluoroscope, a film cassette with an image intensifier screen, and a light box. For decades, progress was incremental. X-ray tubes were improved, film and intensifier were made more sensitive, radiation exposure was reduced, and contrast agents were introduced and improved. It took a second, independent revolution to propel biomedical imaging to today’s level: the invention of the programmable computer and its subsequent miniaturization. The availability of powerful digital data processing hardware and newly developed image processing methods paved the way for new imaging modalities: computed tomography, magnetic resonance imaging, ultrasound imaging, and functional imaging. These new imaging modalities had in common that computer-based data processing was required for image formation. Medical imaging experienced a second wave of rapid progress near the end of the twentieth century when tomography methods were developed and improved. Tomography means imaging by sections, and the origin of the word lies in the Greek τó\muoσ for “to cut” and γρ\′αϕω for “to write.” With the availability of imaging modalities that produced three-dimensional reconstructions of a patient’s body came the need for computerized image processing and computerized image visualization. The expertise of the radiologist in interpreting an image played–and still plays—a major role, but more and more tasks could be given to the computer, and the interpretation of computer-processed images became easier, more objective, and more accurate. Concurrently, scientists became interested in programming computers with the “intelligence” to interpret and understand images. At the same time that computed tomography and magnetic resonance imaging were being invented, new computer methods for image analysis were being introduced.

The long-term vision is computer-aided radiology. Generalized to nonmedical fields, we could call it computer-aided image interpretation. After the first major wave of innovation in the 1970s to 1980s, computer algorithms for image interpretation have become more sophisticated and more complex. New mathematical methods emerged, such as, for example, the wavelet transform and set characterization by the fractal dimension, and were rapidly translated into advanced image analysis methods. Numerical methods that model physical processes—for example, differential equations for diffusion or motion—were applied to image processing tasks as diverse as noise reduction and segmentation. Artificial intelligence models are being used for computer analysis of high-dimensional feature vectors with the purpose of classifying the underlying pixels. Yet the philosopher’s stone of image processing has not been discovered: to make a computer interpret an image with the same flexibility and immunity against artifacts as those of a human observer.

Although this book is well suited as a textbook for graduate-level image processing classes in the computer sciences and engineering fields, it is intended primarily as a reference book. The individual chapters are widely independent of the other chapters, with the exception of Chapters 2 and 3, which provide the foundation of basic image processing operations. The book is a comprehensive hands-on reference of topical image processing methods. Hands-on in this context indicates that not only are the theory, mathematical foundation, and basic description of an image processing operator provided, but performance features, advantages, and limitations are also discussed. Furthermore, key algorithms are provided in pseudocode to assist in implementation. The book aims at making advanced image processing operators accessible to those readers that have a basic familiarity with image processing. As such, the book can be seen as a toolbox in which each tool comes with a complete instruction manual. It is useful for readers who use the tools, because it helps them understand how the tools work. It is also useful for readers who make the tools, as it helps them design and optimize tools for their specific needs. And it is intended as a stepping-stone for those members of the imaging community who are reaching out to develop the next generation of tools.

The application focus of this book is biomedical. However, the same image processing and analysis principles also apply in many other fields. In the environmental and geological sciences, oceanography, soil sciences, forensic sciences, and anthropology, image analysis plays an important role. Satellite imagery and astrophotography, for example, pose the same image processing challenges as those of a magnetic resonance scan: the need to reduce noise, to emphasize the details of interest, and to make the important objects in the image accessible for subjective evaluation or objective measurement.

Acknowledgments

A number of people helped in a significant way to make this book a reality. First and foremost, I want to give my special thanks to my acquisition editor, Wayne Yuhasz, for insisting on this book, for his confidence, and for his continual support throughout the writing process. I would also like to thank George J. Telecki, Lucy Hitz, and Angioline Loredo from John Wiley & Sons for their support during manuscript preparation and production. I particularly appreciated the help of my research specialist, Darcy Lichlyter, who spent many hours and after-hours in the library searching for literature no matter how well hidden it was. Adnan Mustafic, one of my graduate students, showed his competence, initiative, and skill by preparing the live DVD that accompanies this book. Professor Paul J. Friedman kindly provided me with a number of image samples. I also want to express my most sincere thanks to Professor Geoff Dougherty for reviewing the book manuscript and for providing numerous helpful and excellent suggestions for improving the book, as well as to Professor Michael Covington for many additional valuable comments. Last, but not least, I would like to thank my wife, Silke, who not only encouraged me to write the book and who endured some 80 weekends of my quasi-absence, but who also applied her expert skill as an accredited editor in the life sciences to proofread and copyedit the entire manuscript.

MARK A. HAIDEKKER