An edition of Medical image analysis
(2010)
Medical image analysis
2nd ed.
by Atam P. Dhawan
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Medical image analysis
2011, Wiley-Blackwell
electronic resource /
in English
- 2nd ed.
0470918543 9780470918548
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Book Details
Table of Contents
Machine generated contents note: 1.1.Medical Imaging: A Collaborative Paradigm
1.2.Medical Imaging Modalities
1.3.Medical Imaging: from Physiology to Information Processing
1.3.1.Understanding Physiology and Imaging Medium
1.3.2.Physics of Imaging
1.3.3.Imaging Instrumentation
1.3.4.Data Acquisition and Image Reconstruction
1.3.5.Image Analysis and Applications
1.4.General Performance Measures
1.4.1.An Example of Performance Measure
1.5.Biomedical Image Processing and Analysis
1.6.Matlab Image Processing Toolbox
1.6.1.Digital Image Representation
1.6.2.Basic MATLAB Image Toolbox Commands
1.7.Imagepro Interface in Matlab Environment and Image Databases
1.7.1.Imagepro Image Processing Interface
1.7.2.Installation Instructions
1.8.Imagej and Other Image Processing Software Packages
1.9.Exercises
1.10.References
1.11.Definitions
2.1.Image Coordinate System
2.1.1.2-D Image Rotation
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2.1.2.3-D Image Rotation and Translation Transformation
2.2.Linear Systems
2.3.Point Source and Impulse Functions
2.4.Probability and Random Variable Functions
2.4.1.Conditional and Joint Probability Density Functions
2.4.2.Independent and Orthogonal Random Variables
2.5.Image Formation
2.5.1.PSF and Spatial Resolution
2.5.2.Signal-to-Noise Ratio
2.5.3.Contrast-to-Noise Ratio
2.6.Pin-hole Imaging
2.7.Fourier Transform
2.7.1.Sine Function
2.8.Radon Transform
2.9.Sampling
2.10.Discrete Fourier Transform
2.11.Wavelet Transform
2.12.Exercises
2.13.References
3.1.Electromagnetic Radiation
3.2.Electromagnetic Radiation for Image Formation
3.3.Radiation Interaction with Matter
3.3.1.Coherent or Rayleigh Scattering
3.3.2.Photoelectric Absorption
3.3.3.Compton Scattering
3.3.4.Pair Production
3.4.Linear Attenuation Coefficient
3.5.Radiation Detection
3.5.1.Ionized Chambers and Proportional Counters
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3.5.2.Semiconductor Detectors
3.5.3.Advantages of Semiconductor Detectors
3.5.4.Scintillation Detectors
3.6.Detector Subsystem Output Voltage Pulse
3.7.Exercises
3.8.References
4.1.X-Ray Imaging
4.2.X-Ray Generation
4.3.X-Ray 2-D Projection Imaging
4.4.X-Ray Mammography
4.5.X-Ray CT
4.6.Spiral X-Ray CT
4.7.Contrast Agent, Spatial Resolution, and SNR
4.8.Exercises
4.9.References
5.1.MRI Principles
5.2.MR Instrumentation
5.3.MRI Pulse Sequences
5.3.1.Spin-Echo Imaging
5.3.2.Inversion Recovery Imaging
5.3.3.Echo Planar Imaging
5.3.4.Gradient Echo Imaging
5.4.Flow Imaging
5.5.fMRI
5.6.Diffusion Imaging
5.7.Contrast, Spatial Resolution, and SNR
5.8.Exercises
5.9.References
6.1.Radioactivity
6.2.SPECT
6.2.1.Detectors and Data Acquisition System
6.2.2.Contrast, Spatial Resolution, and Signal-to-Noise Ratio in SPELT Imaging
6.3.PET
6.3.1.Detectors and Data Acquisition Systems
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6.3.2.Contrast, Spatial Resolution, and SNR in PET Imaging
6.4.Dual-Modality Spect[-]CT and PET[-]CT Scanners
6.5.Exercises
6.6.References
7.1.Propagation of Sound in a Medium
7.2.Reflection and Refraction
7.3.Transmission of Ultrasound Waves in a Multilayered Medium
7.4.Attenuation
7.5.Ultrasound Reflection Imaging
7.6.Ultrasound Imaging Instrumentation
7.7.Imaging with Ultrasound: A-Mode
7.8.Imaging with Ultrasound: M-Mode
7.9.Imaging with Ultrasound: B-Mode
7.10.Doppler Ultrasound Imaging
7.11.Contrast, Spatial Resolution, and SNR
7.12.Exercises
7.13.References
8.1.Radon Transform and Image Reconstruction
8.1.1.The Central Slice Theorem
8.1.2.Inverse Radon Transform
8.1.3.Backprojection Method
8.2.Iterative Algebraic Reconstruction Methods
8.3.Estimation Methods
8.4.Fourier Reconstruction Methods
8.5.Image Reconstruction in Medical Imaging Modalities
8.5.1.Image Reconstruction in X-Ray CT
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8.5.2.Image Reconstruction in Nuclear Emission Computed Tomography: SPECT and PET
8.5.2.1.A General Approach to ML[-]EM Algorithms
8.5.2.2.A Multigrid EM Algorithm
8.5.3.Image Reconstruction in Magnetic Resonance Imaging
8.5.4.Image Reconstruction in Ultrasound Imaging
8.6.Exercises
8.7.References
9.1.Spatial Domain Methods
9.1.1.Histogram Transformation and Equalization
9.1.2.Histogram Modification
9.1.3.Image Averaging
9.1.4.Image Subtraction
9.1.5.Neighborhood Operations
9.1.5.1.Median Filter
9.1.5.2.Adaptive Arithmetic Mean Filter
9.1.5.3.Image Sharpening and Edge Enhancement
9.1.5.4.Feature Enhancement Using Adaptive Neighborhood Processing
9.2.Frequency Domain Filtering
9.2.1.Wiener Filtering
9.2.2.Constrained Least Square Filtering
9.2.3.Low-Pass Filtering
9.2.4.High-Pass Filtering
9.2.5.Homomorphic Filtering
9.3.Wavelet Transform for Image Processing
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9.3.1.Image Smoothing and Enhancement Using Wavelet Transform
9.4.Exercises
9.5.References
10.1.Edge-Based Image Segmentation
10.1.1.Edge Detection Operations
10.1.2.Boundary Tracking
10.1.3.Hough Transform
10.2.Pixel-Based Direct Classification Methods
10.2.1.Optimal Global Thresholding
10.2.2.Pixel Classification Through Clustering
10.1.2.1.Data Clustering
10.2.2.2.k-Means Clustering
10.2.2.3.Fuzzy c-Means Clustering
10.2.2.4.An Adaptive FCM Algorithm
10.3.Region-Based Segmentation
10.3.1.Region-Growing
10.3.2.Region-Splitting
10.4.Advanced Segmentation Methods
10.4.1.Estimation-Model Based Adaptive Segmentation
10.4.2.Image Segmentation Using Neural Networks
10.4.2.1.Backpropagation Neural Network for Classification
10.4.2.2.The RBF Network
10.4.2.3.Segmentation of Arterial Structure in Digital Subtraction Angiograms
10.5.Exercises
10.6.References
11.1.Feature Extraction and Representation
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11.1.1.Statistical Pixel-Level Features
11.1.2.Shape Features
11.1.2.1.Boundary Encoding: Chain Code
11.1.2.2.Boundary Encoding: Fourier Descriptor
11.1.2.3.Moments for Shape Description
11.1.2.4.Morphological Processing for Shape Description
11.1.3.Texture Features
11.1.4.Relational Features
11.2.Feature Selection for Classification
11.2.1.Linear Discriminant Analysis
11.2.2.PCA
11.2.3.GA-Based Optimization
11.3.Feature and Image Classification
11.3.1.Statistical Classification Methods
11.3.1.1.Nearest Neighbor Classifier
11.3.1.2.Bayesian Classifier
11.3.2.Rule-Based Systems
11.3.3.Neural Network Classifiers
11.3.3.1.Neuro-Fuzzy Pattern Classification
11.3.4.Support Vector Machine for Classification
11.4.Image Analysis and Classification Example: "Difficult-To-Diagnose" Mammographic Microcalcifications
11.5.Exercises
11.6.References
12.1.Rigid-Body Transformation
12.1.1.Affine Transformation
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12.2.Principal Axes Registration
12.3.Iterative Principal Axes Registration
12.4.Image Landmarks and Features-Based Registration
12.4.1.Similarity Transformation for Point-Based Registration
12.4.2.Weighted Features-Based Registration
12.5.Elastic Deformation-Based Registration
12.6.Exercises
12.7.References
13.1.Feature-Enhanced 2-D Image Display Methods
13.2.Stereo Vision and Semi-3-D Display Methods
11.3.Surface- and Volume-Based 3-D Display Methods
13.3.1.Surface Visualization
13.3.2.Volume Visualization
13.4.VR-Based Interactive Visualization
13.4.1.Virtual Endoscopy
13.5.Exercises
13.6.References
14.1.Multiparameter Medical Imaging and Analysis
14.2.Targeted Imaging
14.3.Optical Imaging and Other Emerging Modalities
14.3.1.Optical Microscopy
14.3.2.Optical Endoscopy
14.3.3.Optical Coherence Tomography
14.3.4.Diffuse Reflectance and Transillumination Imaging
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14.3.5.Photoacoustic Imaging: An Emerging Technology
14.4.Model-Based and Multiscale Analysis
14.5.References.
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Edition Notes
Previous ed.: New York; Chichester: Wiley, 2003.
Electronic reproduction. Piscataway, N.J. : IEEE, 2011. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on Feb. 10, 2011). Access may be restricted to users at subscribing institutions.
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