Advanced Biomedical Image Analysis (PDF)

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

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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