Applied Medical Image Processing: A Basic Course, Second Edition
by Wolfgang Birkfellner
Applied Medical Image Processing, 2nd Edition
- 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 1.1
- 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
- Table 1.1
- 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
- Figure 3.1
- 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
- Figure 4.1
- Table 4.1
- 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 5.1
- 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 6.1
- 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 8.1
- 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 9.1
- 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
- Table 9.1
- 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 10.1
- 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
- 11.1 A Hands-on Approach to Camera Calibration and Image-Guided Therapy
- 11.2 Transformations
- 11.3 Camera Calibration
- 11.4 Image-Guided Therapy, Introduction
- 11.5 Image-Guided Therapy, Navigation System
- 11.6 Image-Guided Therapy, Theory in Practice
- 11.7 Summary and Further References
- Figure 11.1
- Table 11.1
- Chapter 12 A Selection of MATLAB® Commands
- Glossary
- MATLAB sample scripts
- Epilogue
- Figure A.1
-
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