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Advanced Biomedical Image Analysis

ISBN: 978-0-470-62458-6
528 pages
December 2010
Advanced Biomedical Image Analysis (0470624582) cover image
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. The accompanying live DVD contains a selection of image analysis software, and it provides most of the algorithms from the book so readers can immediately put their new knowledge to use.

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.

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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.


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.


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.


4 The Wavelet 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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


Appendix A: Image Analysis with Crystal Image.

Appendix B: Software on DVD.


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MARK A. HAIDEKKER, PhD, is an associate professor in the Faculty of Engineering at the University of Georgia. Dr. Haidekker develops image analysis methods based on modern imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and optical imaging. Specific areas of his research include the relationship between images of organs and their biomechanical properties, the development and improvement of laser-based imaging modalities, and the development of methods to image microviscosity and micro-flow patterns with mechanosensitive fluorescent molecular rotors.
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“It is a very good introduction to medical imaging as a whole and contains a lot of literature references for further reading for anyone with a specific interest in one of its parts.”  (Zentralblatt MATH, 1 December 2012)

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