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Spie Press Book

Color Image Processing with Biomedical Applications
Author(s): Rangaraj M. Rangayyan; Begoña Acha; Carmen Serrano
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Book Description

This full-color book begins with a detailed study of the nature of color images-including natural, multispectral, and pseudocolor images-and covers acquisition, quality control, and display of color images, as well as issues of noise and artifacts in color images and segmentation for the detection of regions of interest or objects.

The book is primarily written with the (post-)graduate student in mind, but practicing engineers, researchers, computer scientists, information technologists, medical physicists, and data-processing specialists will also benefit from its depth of information. Those working in diverse areas such as DIP, computer vision, pattern recognition, telecommunications, seismic and geophysical applications, biomedical applications, hospital information systems, remote sensing, mapping, and geomatics may find this book useful in their quest to learn advanced techniques for the analysis of color or multichannel images.

Book Details

Date Published: 12 August 2011
Pages: 434
ISBN: 9780819485649
Volume: PM206

Table of Contents
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Symbols and Abbreviations
1 The Nature and Representation of Color Images
1.1 Color Perception by the Human Visual System
    1.1.1 The radiant spectrum
    1.1.2 Spectral luminous efficiency
    1.1.3 Photometric quantities
    1.1.4 Effects of light sources and illumination
    1.1.5 Color perception and trichromacy
    1.1.6 Color attributes
    1.1.7 Color-matching functions
    1.1.8 Factors affecting color perception
1.2 Representation of Color
    1.2.1 Device-independent color spaces and CIE standards
    1.2.2 Device-dependent color spaces
    1.2.3 Color order systems and the Munsell color system
    1.2.4 Color-difference formulas
1.3 Illustrations of Color Images and their Characteristics
    1.3.1 RGB components and their characteristics
    1.3.2 HSI components and their characteristics
    1.3.3 Chromatic and achromatic pixels
    1.3.4 Histograms of HSI components
    1.3.5 CMYK Ccomponents and their characteristics
1.4 Natural color, Pseudocolor, Stained, Color-Coded, and Multispectral Images
    1.4.1 Pseudocolor images of weather maps
    1.4.2 Staining
    1.4.3 Color coding
    1.4.4 Multispectral imaging
1.5 Biomedical Application: Images of the Retina
1.6 Biomedical Application: Images of Dermatological Lesions
1.7 Remarks
2 Acquisition, Quality Control, and Display of Color Images
2.1 Basics of Color Image Acquisition
    2.1.1 Color image sensors
    2.1.2 Dark current correction
    2.1.3 Demosaicking
    2.1.4 White balance
    2.1.5 Color transformation to unrendered color spaces
    2.1.6 Color transformation to rendered color spaces
2.2 Quality and Information Content of Color Images
    2.2.1 Measures of fidelity
    2.2.2 Factors affecting perceived image quality: contrast, sharpness, and colorfulness
2.3 Calibration and Characterization of Color Images
    2.3.1 Calibration of a digital still camera
    2.3.2 Characterization of a digital still camera
    2.3.3 International Color Consortium profiles
2.4 Natural and Artificial Color in Biomedical Imaging
    2.4.1 Staining in pathology and cytology
    2.4.2 Use of fluorescent dyes in confocal microscopy
    2.4.3 Color in fusion of multimodality images
    2.4.4 Color coding in Doppler ultrasonography
    2.4.5 Use of color in white-matter tractography
2.5 Biomedical Application: Endoscopy of the Digestive Tract
2.6 Biomedical Application: Imaging of Burn Wounds
    2.6.1 Influence of different illumination conditions
    2.6.2 Colorimetric characterization of the camera
2.7 Remarks
3 Removal of Noise and Artifacts
3.1 Space-domain Filters Based on Local Statistics
    3.1.1 The mean filter
    3.1.2 The median filter
    3.1.3 Filters based on order statistics
3.2 Ordering Procedures for Multivariate or Vectorial Data
    3.2.1 Marginal ordering
    3.2.2 Conditional ordering
    3.2.3 Reduced ordering
3.3 The Vector Median and Vector Directional Filters
    3.3.1 Extensions to the VMF and VDF
    3.3.2 The double-window modified trimmed mean filter
    3.3.3 The generalized VDF - double-window - α-trimmed mean filter
3.4 Adaptive Filters
    3.4.1 The adaptive nonparametric filter with Gaussian kernel
    3.4.2 The adaptive hybrid multivariate filter
3.5 The Adaptive-Neighborhood Filter
    3.5.1 Design of the ANF for color images
    3.5.2 Region-growing techniques
    3.5.3 Estimation of the noise-free seed pixel
    3.5.4 Illustrations of application
3.6 Biomedical Application: Removal of Noise due to Dust in Fundus Images of the Retina
3.7 Remarks
4 Enhancement of Color Images
4.1 Componentwise Enhancement of Color Images
    4.1.1 Image enhancement in the RGB and HSI domains
    4.1.2 Hue-preserving contrast enhancement
    4.1.3 Enhancement of saturation
    4.1.4 Selective reduction of saturation
    4.1.5 Alteration of hue
4.2 Correction of Tone and Color Balance
4.3 Filters for Image Sharpening
    4.3.1 Unsharp masking
    4.3.2 Subtracting Laplacian
4.4 Contrast Enhancement
4.5 Color Histogram Equalization and Modification
    4.5.1 Componentwise histogram equalization
    4.5.2 3D histogram equalization
    4.5.3 Histogram explosion
    4.5.4 Histogram decimation
    4.5.5 Adaptive-neighborhood histogram equalization
    4.5.6 Comparative analysis of methods for color histogram equalization
4.6 Pseudocolor Transforms for Enhanced Display of Medical Images
4.7 The Gamut Problem in the Enhancement and Display of Color Images
4.8 Biomedical Application: Correction of Nonuniform Illumination in Fundus Images of the Retina
4.9 Remarks
5 Segmentation of Color Images
5.1 Histogram-based Thresholding
    5.1.1 Thresholding of gray-scale images
    5.1.2 Thresholding of color images
5.2 Color Clustering
    5.2.1 Color feature spaces and distance measures
    5.2.2 Algorithms to partition a feature space
5.3 Detection of Edges
    5.3.1 Edge detectors extended from grayscale to color
    5.3.2 Vectorial approaches
5.4 Region Growing in Color Images
    5.4.1 Seed selection
    5.4.2 Belonging conditions
    5.4.3 Stopping condition
5.5 Morphological Operators for Segmentation of Color Images
    5.5.1 The watershed algorithm for grayscale images
    5.5.2 The watershed algorithm applied to color images
5.6 Biomedical Application: Segmentation of Burn Images
5.7 Biomedical Application: Analysis of the Tissue Composition of Skin Lesions
5.8 Biomedical Application: Segmentation of Blood Vessels in the Retina
    5.8.1 Gabor filters
    5.8.2 Detection of retinal blood vessels
    5.8.3 Dataset of retinal images and preprocessing
    5.8.4 Single-scale filtering and analysis
    5.8.5 Multiscale filtering and analysis
    5.8.6 Use of multiple color components for improved detection of retinal blood vessels
    5.8.7 Distinguishing between retinal arteries and veins
5.9 Biomedical Application: Segmentation of Histopathology Images
    5.9.1 Color separation in histopathology images
    5.9.2 Segmentation of lumen in histopathology images
    5.9.3 Detection of tubules in histopathology images
5.10 Remarks
6 Afterword
About the Authors

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