7th Edition

The Image Processing Handbook

By John C. Russ, F. Brent Neal Copyright 2016
    1056 Pages
    by CRC Press

    1053 Pages 1175 Color Illustrations
    by CRC Press

    1053 Pages 1175 Color Illustrations
    by CRC Press

    Consistently rated as the best overall introduction to computer-based image processing, The Image Processing Handbook covers two-dimensional (2D) and three-dimensional (3D) imaging techniques, image printing and storage methods, image processing algorithms, image and feature measurement, quantitative image measurement analysis, and more.

    Incorporating image processing and analysis examples at all scales, from nano- to astro-, this Seventh Edition:

    • Features a greater range of computationally intensive algorithms than previous versions
    • Provides better organization, more quantitative results, and new material on recent developments
    • Includes completely rewritten chapters on 3D imaging and a thoroughly revamped chapter on statistical analysis
    • Contains more than 1700 references to theory, methods, and applications in a wide variety of disciplines
    • Presents 500+ entirely new figures and images, with more than two-thirds appearing in color

    The Image Processing Handbook, Seventh Edition delivers an accessible and up-to-date treatment of image processing, offering broad coverage and comparison of algorithms, approaches, and outcomes.

    About this text
    A word of caution
    A personal note

    Acquiring Images
    Human reliance on images
    Extracting information
    Video cameras
    CCD cameras
    CMOS detectors
    Camera artifacts and limitations
    Color cameras
    Camera resolution
    Electronics and bandwidth limitations
    Handling color data
    Color encoding
    Other image sources
    Tonal resolution
    The image contents
    Camera limitations
    High-depth images
    Color displays
    Image types
    Multiple images
    Imaging requirements

    Printing and Storage
    Hard copies
    Dots on paper
    Color printing
    Adding black—CMYK
    Printing hardware
    Film recorders
    Presentation tools
    File storage
    Storage media
    Magnetic recording
    Databases for images
    Searching by content
    Browsing and thumbnails
    File formats
    Lossless coding
    Reduced color palettes
    JPEG compression
    Wavelet compression
    Fractal compression
    Digital movies

    Human Vision
    What we see and why
    Technical specs
    Seeing color
    What the eye tells the brain
    Spatial comparisons
    Local to global hierarchies
    It’s about time
    The third dimension
    How versus what
    Seeing what isn’t there, and vice versa
    Image compression
    A world of light
    Size matters
    Shape (whatever that means)
    Arrangements must be made
    Seeing is believing
    Learning more

    Correcting Imaging Defects
    Color adjustments
    Hue, saturation, intensity
    Other spaces
    Color correction
    Noisy images
    Neighborhood averaging
    Gaussian smoothing
    Neighborhood ranking
    The color median
    More median filters
    Weighted, conditional, and adaptive neighborhoods
    Other neighborhood noise reduction methods
    Defect removal, maximum entropy, and maximum likelihood
    Nonuniform illumination
    Fitting a background function
    Rank leveling
    Color images
    Nonplanar views
    Computer graphics
    Geometric distortion

    Image Enhancement in the Spatial Domain
    Purposes for enhancement
    Contrast expansion
    False color lookup tables (LUTs)
    Contrast manipulation
    Histogram equalization
    Contrast in color images
    Local equalization
    Laplacian sharpening
    The unsharp mask
    Edges and gradients
    Edge orientation
    More edge detectors
    Rank-based methods
    Implementation notes
    Image math
    Subtracting images
    Multiplication and division
    Principal component analysis
    Principal component analysis for contrast enhancement
    Other image combinations

    Processing Images in Frequency Space
    About frequency space
    The Fourier transform
    Fourier transforms of simple functions
    Moving to two dimensions
    Frequencies and spacings
    Preferred orientation
    Texture and fractals
    Removing selected frequencies
    Periodic noise removal
    Selection of periodic information
    Noise and Wiener deconvolution
    Other deconvolution methods
    Additional notes on deconvolution
    Template matching and correlation

    Segmentation and Thresholding
    Brightness thresholding
    Automatic settings
    Multiband images
    Color thresholding
    Thresholding from texture
    Multiple thresholding criteria
    Textural orientation
    Region boundaries
    Noise and overlaps
    Selecting smooth boundaries
    Conditional histograms
    Boundary lines
    Cluster analysis
    More segmentation methods
    Image representation

    Processing Binary Images
    Boolean operations
    Combining Boolean operations
    From pixels to features
    Filling holes
    Measurement grids
    Boolean logic with features
    Selecting features by location
    Double thresholding
    Erosion and dilation
    Opening and closing
    Measurements using erosion and dilation
    Extension to grayscale images
    Neighborhood parameters
    Examples of use
    Euclidean distance map
    Watershed segmentation
    Ultimate eroded points
    Boundary lines
    Combining skeleton and Euclidean distance map

    Image Measurements
    Global measurements
    Surface area
    Grain size
    Multiple surfaces
    Sampling strategies
    Determining number
    Curvature, connectivity, and the Disector
    Anisotropy and gradients
    Size distribution
    Classical stereology (unfolding)

    Feature Measurements
    Brightness measurements
    Brightness profiles
    Color values
    Determining location
    Neighbor relationships
    Separation distance
    The linear Hough transform
    The circular Hough transform
    Special counting procedures
    Feature size
    Circles and ellipses
    Caliper dimensions

    Characterizing Shape
    Describing shape
    Dimensionless ratios
    Effects of orientation
    "Like a circle"
    An example: Leaves
    Topology and the skeleton
    Shock graphs
    Fractal dimension
    Measurement techniques
    Harmonic analysis
    Chain code
    An example: Arrow points
    An example: Dandelion
    Zernike moments

    Correlation, Classification, Identification, and Matching
    A variety of purposes
    Curvature scale space
    Distributions and decision points
    Linear discriminant analysis (LDA) and principal component analysis (PCA)
    Class definition
    Unsupervised learning
    Are groups different?
    Neural nets
    k-Nearest neighbors
    Parametric description
    Bayesian statistics
    A comparison
    Harmonic analysis and invariant moments
    Species examples
    Landmark data

    3D Imaging
    More than two dimensions
    Volume imaging versus sections
    Serial sections
    Removing layers
    Confocal microscopy
    Stereo viewing
    Tomographic reconstruction
    Reconstruction artifacts
    Algebraic reconstruction
    Maximum entropy
    Imaging geometries
    Other signals
    Beam hardening and other issues
    3D tomography
    Dual energy methods
    3D reconstruction and visualization
    Slices and surfaces
    Marching cubes
    Volumetric displays
    Ray tracing

    3D Processing and Measurement
    Processing voxel arrays
    When the z-axis is different
    Multiple image sets
    Thresholding and segmentation
    Morphological operations and structural measurements
    Surface and volume
    Quantitative use of reconstructions
    Methods for object measurements
    Examples of object measurements
    Other object measurements
    Industrial applications
    Comparison to stereological measurements
    Spherical harmonics, wavelets, and fractal dimension
    Other applications and future possibilities

    Imaging Surfaces
    Producing surfaces
    Imaging by physical contact
    Noncontacting measurements
    Shape from shading and polynomial texture map
    Microscopy of surfaces
    Matching points
    Composition imaging
    Processing of range images
    Processing of composition maps
    Data presentation and visualization
    Surface rendering
    Representing elevation data
    The surface measurement suite
    Hybrid properties
    Topographic analysis
    Fractal dimensions



    John C. Russ has used image processing and analysis as a principal tool for understanding and characterizing the structure and function of materials throughout his more than 50-year career as a scientist and educator. Much of Russ' research work has been concerned with the microstructure and surface topography of metals and ceramics. He has received funding for his research from government agencies and from industry. Teaching the principles and methods involved to several thousand students—in addition to consulting for many industrial clients—has further broadened Dr. Russ’ experience and the scope of applications for image processing and analysis. He continues to write and consult for a variety of companies (and to provide expert testimony in criminal and civil cases). He also still teaches image processing and analysis workshops worldwide and reviews publications and funding proposals.

    F. Brent Neal is a scientist and industrial researcher with Milliken Research Corporation, where he currently leads the central materials characterization and analytical chemistry facility. In this role, he leads efforts in technology and product development through deep understanding of materials performance. He has three patents issued or pending based on his work in polymer-matrix composites. Prior to his tenure at Milliken Research Corporation, he consulted and developed bespoke software for quantitative image analysis. He received his Ph.D in solid-state physics from Louisiana State University in 2002. Over the course of his career, he has measured and analyzed images from many different fields and his experience in materials characterization and measurement has been applied everywhere from the lab bench to manufacturing plants.

    "With a new co-author (the same Brent Neal who has collaborated with him before in writing the excellent book Measuring Shape), John Russ has again produced a winner—a textbook and reference book that belongs on the shelf, and perhaps on the desk, of anyone involved in digital imaging. Even if you have a copy of one of the previous editions, this is a highly worthwhile addition."
    Microscopy and Microanalysis, October 2016