Book description
A widely used, classroom-tested text, Applied Medical Image Processing: A Basic Course delivers an ideal introduction to image processing in medicine, emphasizing the clinical relevance and special requirements of the field. Avoiding excessive mathematical formalisms, the book presents key principles by implementing algorithms from scratch and using simple MATLAB®/Octave scripts with image data and illustrations on downloadable resources or companion website. Organized as a complete textbook, it provides an overview of the physics of medical image processing and discusses image formats and data storage, intensity transforms, filtering of images and applications of the Fourier transform, three-dimensional spatial transforms, volume rendering, image registration, and tomographic reconstruction.
This Second Edition of the bestseller:
- Contains two brand-new chapters on clinical applications and image-guided therapy
- Devotes more attention to the subject of color space
- Includes additional examples from radiology, internal medicine, surgery, and radiation therapy
- Incorporates freely available programs in the public domain (e.g., GIMP, 3DSlicer, and ImageJ) when applicable
Beneficial to students of medical physics, biomedical engineering, computer science, applied mathematics, and related fields, as well as medical physicists, radiographers, radiologists, and other professionals, Applied Medical Image Processing: A Basic Course, Second Edition is fully updated and expanded to ensure a perfect blend of theory and practice.
Table of contents
- Preliminaries
- Dedication
- Foreword
- Preface to the First Edition
- Preface to the Second Edition
- User Guide
- Acknowledgments
-
Chapter 1 A Few Basics of Medical Image Sources
- 1.1 Radiology
- 1.2 The Electromagnetic Spectrum
- 1.3 Basic X-Ray Physics
- 1.4 Attenuation and Imaging
- 1.5 Computed Tomography
- 1.6 Magnetic Resonance Tomography
- 1.7 Ultrasound
- 1.8 Nuclear Medicine and Molecular Imaging
- 1.9 Other Imaging Techniques
- 1.10 Radiation Protection and Dosimetry
- 1.11 Summary and Further References
-
- Figure 1.1
- Figure 1.2
- Figure 1.3
- Figure 1.4
- Figure 1.5
- Figure 1.6
- Figure 1.7
- Figure 1.8
- Figure 1.9
- Figure 1.10
- Figure 1.11
- Figure 1.12
- Figure 1.13
- Figure 1.14
- Figure 1.15
- Figure 1.16
- Figure 1.17
- Figure 1.18
- Figure 1.19
- Figure 1.20
- Figure 1.21
- Figure 1.22
- Figure 1.23
- Figure 1.24
- Figure 1.25
- Figure 1.26
- Figure 1.27
- Figure 1.28
- Chapter 2 Image Processing in Clinical Practice
-
Chapter 3 Image Representation
- 3.1 Pixels and Voxels
- 3.2 Gray Scale and Color Representation
- 3.3 Image File Formats
- 3.4 Dicom
- 3.5 Other Formats – Analyze 7.5, Nifti and Interfile
- 3.6 Image Quality and the Signal-to-Noise Ratio
- 3.7 Practical Lessons
- 3.8 Summary and Further References
-
Chapter 4 Operations in Intensity Space
- 4.1 The Intensity Transform Function and The Dynamic Range
- 4.2 Windowing
- 4.3 Histograms and Histogram Operations
- 4.4 Dithering and Depth
-
4.5 Practical Lessons
- 4.5.1 Linear adjustment of image depth range
- 4.5.2 Composing a color image from grayscale images
- 4.5.3 Improving visibility of low-contrast detail – taking the logarithm
- 4.5.4 Modelling a general nonlinear transfer function – the Sigmoid
- 4.5.5 Histograms and histogram operations
- 4.5.6 Automatic optimization of image contrast using the histogram
- 4.5.7 Intensity operations using ImageJ and 3DSlicer
- 4.6 Summary and Further References
-
Chapter 5 Filtering and Transformations
- 5.1 The Filtering Operation
- 5.2 The Fourier Transform
- 5.3 Other Transforms
-
5.4 Practical Lessons
- 5.4.1 Kernel – based low pass and high pass filtering
- 5.4.2 Basic filtering operations in ImageJ
- 5.4.3 Numerical differentiation
- 5.4.4 Unsharp masking
- 5.4.5 The median filter
- 5.4.6 Some properties of the Fourier-transform
- 5.4.7 Frequency filtering in Fourier-space on images
- 5.4.8 Applied convolution – PSF and the MTF
- 5.4.9 Determination of system resolution of an Anger-camera using a point source
- 5.4.10 The Hough transform
- 5.4.11 The distance transform
- 5.5 Summary and Further References
-
- Figure 5.1
- Figure 5.2
- Figure 5.3
- Figure 5.4
- Figure 5.5
- Figure 5.6
- Figure 5.7
- Figure 5.8
- Figure 5.9
- Figure 5.10
- Figure 5.11
- Figure 5.12
- Figure 5.13
- Figure 5.14
- Figure 5.15
- Figure 5.16
- Figure 5.17
- Figure 5.18
- Figure 5.19
- Figure 5.20
- Figure 5.21
- Figure 5.22
- Figure 5.23
- Figure 5.24
- Figure 5.25
- Figure 5.26
- Figure 5.27
- Figure 5.28
- Figure 5.29
- Figure 5.30
- Figure 5.31
- Figure 5.32
- Figure 5.33
- Figure 5.34
- Figure 5.35
- Figure 5.36
- Figure 5.37
- Figure 5.38
- Figure 5.39
- Figure 5.40
- Figure 5.41
- Figure 5.42
- Figure 5.43
- Figure 5.44
- Figure 5.45
- Figure 5.46
- Figure 5.47
- Figure 5.48
- Figure 5.49
-
Chapter 6 Segmentation
- 6.1 The Segmentation Problem
- 6.2 ROI Definition and Centroids
- 6.3 Thresholding
- 6.4 Region Growing
- 6.5 More Sophisticated Segmentation Methods
- 6.6 Morphological Operations
- 6.7 Evaluation of Segmentation Results
-
6.8 Practical Lessons
- 6.8.1 Count rate evaluation by ROI selection
- 6.8.2 Region definition by global thresholding
- 6.8.3 Region growing
- 6.8.4 Region growing in 3D
- 6.8.5 A very simple snake-type example
- 6.8.6 Erosion and dilation
- 6.8.7 Hausdorff-distances and Dice-coefficients
- 6.8.8 Improving segmentation results by filtering
- 6.9 Summary and Further References
-
- Figure 6.1
- Figure 6.2
- Figure 6.3
- Figure 6.4
- Figure 6.5
- Figure 6.6
- Figure 6.7
- Figure 6.8
- Figure 6.9
- Figure 6.10
- Figure 6.11
- Figure 6.12
- Figure 6.13
- Figure 6.14
- Figure 6.15
- Figure 6.16
- Figure 6.17
- Figure 6.18
- Figure 6.19
- Figure 6.20
- Figure 6.21
- Figure 6.22
- Figure 6.23
- Figure 6.24
- Figure 6.25
- Figure 6.26
- Figure 6.27
- Figure 6.28
- Figure 6.29
- Figure 6.30
- Chapter 7 Spatial Transforms
-
Chapter 8 Rendering and Surface Models
- 8.1 Visualization
- 8.2 Orthogonal and Perspective Projection, And the Viewpoint
- 8.3 Raycasting
- 8.4 Surface-Based Rendering
-
8.5 Practical Lessons
- 8.5.1 A perspective example
- 8.5.2 Simple orthogonal raycasting
- 8.5.3 Viewpoint transforms and splat rendering
- 8.5.4 Volume rendering using color coding
- 8.5.5 A simple surface rendering - depth shading
- 8.5.6 Rendering of voxel surfaces
- 8.5.7 A rendering example using 3DSlicer
- 8.5.8 Extracting a surface using the cuberille algorithm
- 8.5.9 A demonstration of shading effects
- 8.6 Summary and Further References
-
- Figure 8.1
- Figure 8.2
- Figure 8.3
- Figure 8.4
- Figure 8.5
- Figure 8.6
- Figure 8.7
- Figure 8.8
- Figure 8.9
- Figure 8.10
- Figure 8.11
- Figure 8.12
- Figure 8.13
- Figure 8.14
- Figure 8.15
- Figure 8.16
- Figure 8.17
- Figure 8.18
- Figure 8.19
- Figure 8.20
- Figure 8.21
- Figure 8.22
- Figure 8.23
- Figure 8.24
- Figure 8.25
- Figure 8.26
- Figure 8.27
- Figure 8.28
- Figure 8.29
- Figure 8.30
- Figure 8.31
- Figure 8.32
- Figure 8.33
- Figure 8.34
- Figure 8.35
-
Chapter 9 Registration
- 9.1 Fusing Information
- 9.2 Registration Paradigms
- 9.3 Merit Functions
- 9.4 Optimization Strategies
- 9.5 Some General Comments
- 9.6 Camera Calibration
- 9.7 Registration to Physical Space
- 9.8 Evaluation of Registration Results
- 9.9 Practical Lessons
- 9.10 Summary and Further References
-
- Figure 9.1
- Figure 9.2
- Figure 9.3
- Figure 9.4
- Figure 9.5
- Figure 9.6
- Figure 9.7
- Figure 9.8
- Figure 9.9
- Figure 9.10
- Figure 9.11
- Figure 9.12
- Figure 9.13
- Figure 9.14
- Figure 9.15
- Figure 9.16
- Figure 9.17
- Figure 9.18
- Figure 9.19
- Figure 9.20
- Figure 9.21
- Figure 9.22
- Figure 9.23
- Figure 9.24
- Figure 9.25
- Figure 9.26
- Figure 9.27
-
Chapter 10 CT Reconstruction
- 10.1 Introduction
- 10.2 Radon Transform
- 10.3 Algebraic Reconstruction
- 10.4 Some Remarks on Fourier Transform and Filtering
- 10.5 Filtered Backprojection
- 10.6 Practical Lessons
- 10.7 Summary and Further References
-
- Figure 10.1
- Figure 10.2
- Figure 10.3
- Figure 10.4
- Figure 10.5
- Figure 10.6
- Figure 10.7
- Figure 10.8
- Figure 10.9
- Figure 10.10
- Figure 10.11
- Figure 10.12
- Figure 10.13
- Figure 10.14
- Figure 10.15
- Figure 10.16
- Figure 10.17
- Figure 10.18
- Figure 10.19
- Figure 10.20
- Figure 10.21
- Figure 10.22
- Figure 10.23
- Figure 10.24
- Figure 10.25
- Figure 10.26
- Figure 10.27
- Chapter 11 A Tutorial on Image-Guided Therapy
- Chapter 12 A Selection of MATLAB® Commands
- Glossary
- MATLAB sample scripts
- Epilogue
Product information
- Title: Applied Medical Image Processing, 2nd Edition
- Author(s):
- Release date: April 2016
- Publisher(s): CRC Press
- ISBN: 9781498759724
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