Mark Haidekker

Advanced Biomedical Image Analysis (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 528
• Erscheinungstermin: 22. September 2011
• Englisch
• ISBN-13: 9780470872086
• Artikelnr.: 37299843

Autorenporträt

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.

Inhaltsangabe

Preface. 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. 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. 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. 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. References. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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: Image Analysis with Crystal Image.
Appendix B: Software on DVD. Index.