Cover image for Applied Medical Image Processing, 2nd Edition

Applied Medical Image Processing, 2nd Edition

Author Wolfgang Birkfellner
Release Date: 2016/04/01
ISBN: 9781466555594
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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 usin

Table of Contents

  1. Preliminaries
  2. Dedication
  3. Foreword
  4. Preface to the First Edition
  5. Preface to the Second Edition
  6. User Guide
    1. Literature
  7. Acknowledgments
  8. Chapter 1 A Few Basics of Medical Image Sources
    1. 1.1 Radiology
    2. 1.2 The Electromagnetic Spectrum
    3. 1.3 Basic X-Ray Physics
      1. 1.3.1 The x-ray tube
      2. 1.3.2 X-ray detectors
    4. 1.4 Attenuation and Imaging
    5. 1.5 Computed Tomography
      1. 1.5.1 Basics of CT and scanner generations
      2. 1.5.2 Imaging geometries and CT technologies
      3. 1.5.3 Image artifacts in CT
      4. 1.5.4 Safety aspects
    6. 1.6 Magnetic Resonance Tomography
      1. 1.6.1 Basic MRI physics
        1. 1.6.1.1 Relaxation processes
        2. 1.6.1.2 Spin-echo
      2. 1.6.2 Tissue contrast
      3. 1.6.3 Spatial localization
        1. 1.6.3.1 Slice selection
        2. 1.6.3.2 Frequency encoding
        3. 1.6.3.3 Phase encoding
      4. 1.6.4 Image artifacts in MR
      5. 1.6.5 The elements of an MR system
        1. 1.6.5.1 The main static magnetic field
        2. 1.6.5.2 Gradient fields
        3. 1.6.5.3 The RF system
      6. 1.6.6 Safety aspects
    7. 1.7 Ultrasound
      1. 1.7.1 Some physical properties of sound
      2. 1.7.2 Basic principles
      3. 1.7.3 Image artifacts in B-Mode US imaging
      4. 1.7.4 Doppler effect
      5. 1.7.5 3D imaging
      6. 1.7.6 Safety aspects
    8. 1.8 Nuclear Medicine and Molecular Imaging
      1. 1.8.1 Scintigraphy
      2. 1.8.2 The γ camera
      3. 1.8.3 SPECT and PET
    9. 1.9 Other Imaging Techniques
    10. 1.10 Radiation Protection and Dosimetry
      1. 1.10.1 Terminology of dosimetry
      2. 1.10.2 Radiation effects on tissue and organs
      3. 1.10.3 Natural and man-made radiation exposure
    11. 1.11 Summary and Further References
      1. Literature
    12. Figure 1.1
      1. Figure 1.1
      2. Figure 1.2
      3. Figure 1.3
      4. Figure 1.4
      5. Figure 1.5
      6. Figure 1.6
      7. Figure 1.7
      8. Figure 1.8
      9. Figure 1.9
      10. Figure 1.10
      11. Figure 1.11
      12. Figure 1.12
      13. Figure 1.13
      14. Figure 1.14
      15. Figure 1.15
      16. Figure 1.16
      17. Figure 1.17
      18. Figure 1.18
      19. Figure 1.19
      20. Figure 1.20
      21. Figure 1.21
      22. Figure 1.22
      23. Figure 1.23
      24. Figure 1.24
      25. Figure 1.25
      26. Figure 1.26
      27. Figure 1.27
      28. Figure 1.28
    13. Table 1.1
      1. Table 1.1
      2. Table 1.2
      3. Table 1.3
      4. Table 1.4
      5. Table 1.5
  9. Chapter 2 Image Processing in Clinical Practice
    1. 2.1 Application Examples
    2. 2.2 Image Databases
    3. 2.3 Intensity Operations
    4. 2.4 Filter Operations
    5. 2.5 Segmentation
    6. 2.6 Spatial Transforms
    7. 2.7 Rendering and Surface Models
    8. 2.8 Registration
    9. 2.9 CT Reconstruction
    10. 2.10 Summary
    11. Figure 2.1
      1. Figure 2.1
      2. Figure 2.2
      3. Figure 2.3
      4. Figure 2.4
      5. Figure 2.5
      6. Figure 2.6
      7. Figure 2.7
      8. Figure 2.8
      9. Figure 2.9
      10. Figure 2.10
      11. Figure 2.11
      12. Figure 2.12
      13. Figure 2.13
  10. Chapter 3 Image Representation
    1. 3.1 Pixels and Voxels
      1. 3.1.1 Algebraic image operations
    2. 3.2 Gray Scale and Color Representation
      1. 3.2.1 Depth
      2. 3.2.2 Color and look up tables
    3. 3.3 Image File Formats
    4. 3.4 Dicom
    5. 3.5 Other Formats – Analyze 7.5, Nifti and Interfile
    6. 3.6 Image Quality and the Signal-to-Noise Ratio
    7. 3.7 Practical Lessons
      1. 3.7.1 Image subtraction
      2. 3.7.2 Opening and decoding a single DICOM file in MATLAB
      3. 3.7.3 Opening a DICOM file using ImageJ and 3DSlicer
        1. 3.7.3.1 A single slice in ImageJ
        2. 3.7.3.2 A CT-volume in 3DSlicer
      4. 3.7.4 Converting a color image to gray scale
        1. 3.7.4.1 A note on good MATLAB programming habits
      5. 3.7.5 Computing the SNR of x-ray images as a function of dose
    8. 3.8 Summary and Further References
      1. Literature
    9. Figure 3.1
      1. Figure 3.1
      2. Figure 3.2
      3. Figure 3.3
      4. Figure 3.4
      5. Figure 3.5
      6. Figure 3.6
      7. Figure 3.7
      8. Figure 3.8
      9. Figure 3.9
      10. Figure 3.10
      11. Figure 3.11
      12. Figure 3.12
      13. Figure 3.13
      14. Figure 3.14
      15. Figure 3.15
      16. Figure 3.16
      17. Figure 3.17
      18. Figure 3.18
      19. Figure 3.19
      20. Figure 3.20
      21. Figure 3.21
      22. Figure 3.22
      23. Figure 3.23
  11. Chapter 4 Operations in Intensity Space
    1. 4.1 The Intensity Transform Function and The Dynamic Range
    2. 4.2 Windowing
    3. 4.3 Histograms and Histogram Operations
    4. 4.4 Dithering and Depth
    5. 4.5 Practical Lessons
      1. 4.5.1 Linear adjustment of image depth range
        1. 4.5.1.1 A note on good MATLAB® programming habits
      2. 4.5.2 Composing a color image from grayscale images
      3. 4.5.3 Improving visibility of low-contrast detail – taking the logarithm
      4. 4.5.4 Modelling a general nonlinear transfer function – the Sigmoid
      5. 4.5.5 Histograms and histogram operations
      6. 4.5.6 Automatic optimization of image contrast using the histogram
      7. 4.5.7 Intensity operations using ImageJ and 3DSlicer
    6. 4.6 Summary and Further References
      1. Literature
    7. Figure 4.1
      1. Figure 4.1
      2. Figure 4.2
      3. Figure 4.3
      4. Figure 4.4
      5. Figure 4.5
      6. Figure 4.6
      7. Figure 4.7
      8. Figure 4.8
      9. Figure 4.9
      10. Figure 4.10
      11. Figure 4.11
      12. Figure 4.12
      13. Figure 4.13
      14. Figure 4.14
      15. Figure 4.15
      16. Figure 4.16
      17. Figure 4.17
      18. Figure 4.18
      19. Figure 4.19
      20. Figure 4.20
      21. Figure 4.21
      22. Figure 4.22
    8. Table 4.1
      1. Table 4.1
  12. Chapter 5 Filtering and Transformations
    1. 5.1 The Filtering Operation
      1. 5.1.1 Kernel based smoothing and sharpening operations
      2. 5.1.2 Differentiation and edge detection
      3. 5.1.3 Helpful non-linear filters
    2. 5.2 The Fourier Transform
      1. 5.2.1 Basic linear algebra and series expansions
      2. 5.2.2 Waves - a special orthonormal system
      3. 5.2.3 Some important properties of the Fourier transform
      4. 5.2.4 Image processing in the frequency domain
      5. 5.2.5 Modelling properties of imaging systems - the PSF and the MTF
    3. 5.3 Other Transforms
      1. 5.3.1 The Hough transform
      2. 5.3.2 The distance transform
    4. 5.4 Practical Lessons
      1. 5.4.1 Kernel – based low pass and high pass filtering
      2. 5.4.2 Basic filtering operations in ImageJ
      3. 5.4.3 Numerical differentiation
        1. 5.4.3.1 A note on good MATLAB® programming habits
      4. 5.4.4 Unsharp masking
      5. 5.4.5 The median filter
      6. 5.4.6 Some properties of the Fourier-transform
        1. 5.4.6.1 Spectra of simple functions in k-space
        2. 5.4.6.2 More complex functions
        3. 5.4.6.3 Convolution of simple functions
        4. 5.4.6.4 Differentiation in k-space
      7. 5.4.7 Frequency filtering in Fourier-space on images
      8. 5.4.8 Applied convolution – PSF and the MTF
      9. 5.4.9 Determination of system resolution of an Anger-camera using a point source
      10. 5.4.10 The Hough transform
      11. 5.4.11 The distance transform
    5. 5.5 Summary and Further References
      1. Literature
    6. Figure 5.1
      1. Figure 5.1
      2. Figure 5.2
      3. Figure 5.3
      4. Figure 5.4
      5. Figure 5.5
      6. Figure 5.6
      7. Figure 5.7
      8. Figure 5.8
      9. Figure 5.9
      10. Figure 5.10
      11. Figure 5.11
      12. Figure 5.12
      13. Figure 5.13
      14. Figure 5.14
      15. Figure 5.15
      16. Figure 5.16
      17. Figure 5.17
      18. Figure 5.18
      19. Figure 5.19
      20. Figure 5.20
      21. Figure 5.21
      22. Figure 5.22
      23. Figure 5.23
      24. Figure 5.24
      25. Figure 5.25
      26. Figure 5.26
      27. Figure 5.27
      28. Figure 5.28
      29. Figure 5.29
      30. Figure 5.30
      31. Figure 5.31
      32. Figure 5.32
      33. Figure 5.33
      34. Figure 5.34
      35. Figure 5.35
      36. Figure 5.36
      37. Figure 5.37
      38. Figure 5.38
      39. Figure 5.39
      40. Figure 5.40
      41. Figure 5.41
      42. Figure 5.42
      43. Figure 5.43
      44. Figure 5.44
      45. Figure 5.45
      46. Figure 5.46
      47. Figure 5.47
      48. Figure 5.48
      49. Figure 5.49
  13. Chapter 6 Segmentation
    1. 6.1 The Segmentation Problem
    2. 6.2 ROI Definition and Centroids
    3. 6.3 Thresholding
    4. 6.4 Region Growing
    5. 6.5 More Sophisticated Segmentation Methods
      1. 6.5.1 ITK SNAP – a powerful implementation of a segmentation algorithm at work
    6. 6.6 Morphological Operations
    7. 6.7 Evaluation of Segmentation Results
    8. 6.8 Practical Lessons
      1. 6.8.1 Count rate evaluation by ROI selection
      2. 6.8.2 Region definition by global thresholding
      3. 6.8.3 Region growing
      4. 6.8.4 Region growing in 3D
      5. 6.8.5 A very simple snake-type example
      6. 6.8.6 Erosion and dilation
      7. 6.8.7 Hausdorff-distances and Dice-coefficients
      8. 6.8.8 Improving segmentation results by filtering
        1. 6.8.8.1 Better thresholding
        2. 6.8.8.2 Preprocessing and active contours
    9. 6.9 Summary and Further References
      1. Literature
    10. Figure 6.1
      1. Figure 6.1
      2. Figure 6.2
      3. Figure 6.3
      4. Figure 6.4
      5. Figure 6.5
      6. Figure 6.6
      7. Figure 6.7
      8. Figure 6.8
      9. Figure 6.9
      10. Figure 6.10
      11. Figure 6.11
      12. Figure 6.12
      13. Figure 6.13
      14. Figure 6.14
      15. Figure 6.15
      16. Figure 6.16
      17. Figure 6.17
      18. Figure 6.18
      19. Figure 6.19
      20. Figure 6.20
      21. Figure 6.21
      22. Figure 6.22
      23. Figure 6.23
      24. Figure 6.24
      25. Figure 6.25
      26. Figure 6.26
      27. Figure 6.27
      28. Figure 6.28
      29. Figure 6.29
      30. Figure 6.30
  14. Chapter 7 Spatial Transforms
    1. 7.1 Discretization – Resolution and Artifacts
    2. 7.2 Interpolation and Volume Regularization
    3. 7.3 Translation and Rotation
      1. 7.3.1 Rotation in 2D - some properties of the rotation matrix
      2. 7.3.2 Rotation and translation in 3D
      3. 7.3.3 A special case - the principal axis transform
      4. 7.3.4 The quaternion representation of rotations
    4. 7.4 Reformatting
    5. 7.5 Tracking and Image-Guided Therapy
    6. 7.6 Practical Lessons
      1. 7.6.1 Spatial image transforms in 2D
      2. 7.6.2 Two simple interpolation examples
      3. 7.6.3 A special case – the PCA on binary images
      4. 7.6.4 A geometric reformatting example – conic sections
      5. 7.6.5 A reformatting example on a small volume data set
      6. 7.6.6 Convolution revisited
    7. 7.7 Summary and Further References
      1. Literature
    8. Figure 7.1
      1. Figure 7.1
      2. Figure 7.2
      3. Figure 7.3
      4. Figure 7.4
      5. Figure 7.5
      6. Figure 7.6
      7. Figure 7.7
      8. Figure 7.8
      9. Figure 7.9
      10. Figure 7.10
      11. Figure 7.11
      12. Figure 7.12
      13. Figure 7.13
      14. Figure 7.14
      15. Figure 7.15
      16. Figure 7.16
      17. Figure 7.17
      18. Figure 7.18
      19. Figure 7.19
      20. Figure 7.20
      21. Figure 7.21
  15. Chapter 8 Rendering and Surface Models
    1. 8.1 Visualization
    2. 8.2 Orthogonal and Perspective Projection, And the Viewpoint
    3. 8.3 Raycasting
      1. 8.3.1 MIP, DRRs and volume rendering
      2. 8.3.2 Other rendering techniques
    4. 8.4 Surface-Based Rendering
      1. 8.4.1 Surface extraction, file formats for surfaces, shading and textures
      2. 8.4.2 Shading models
      3. 8.4.3 A special application – virtual endoscopy
    5. 8.5 Practical Lessons
      1. 8.5.1 A perspective example
      2. 8.5.2 Simple orthogonal raycasting
      3. 8.5.3 Viewpoint transforms and splat rendering
      4. 8.5.4 Volume rendering using color coding
      5. 8.5.5 A simple surface rendering - depth shading
      6. 8.5.6 Rendering of voxel surfaces
      7. 8.5.7 A rendering example using 3DSlicer
      8. 8.5.8 Extracting a surface using the cuberille algorithm
      9. 8.5.9 A demonstration of shading effects
    6. 8.6 Summary and Further References
      1. Literature
    7. Figure 8.1
      1. Figure 8.1
      2. Figure 8.2
      3. Figure 8.3
      4. Figure 8.4
      5. Figure 8.5
      6. Figure 8.6
      7. Figure 8.7
      8. Figure 8.8
      9. Figure 8.9
      10. Figure 8.10
      11. Figure 8.11
      12. Figure 8.12
      13. Figure 8.13
      14. Figure 8.14
      15. Figure 8.15
      16. Figure 8.16
      17. Figure 8.17
      18. Figure 8.18
      19. Figure 8.19
      20. Figure 8.20
      21. Figure 8.21
      22. Figure 8.22
      23. Figure 8.23
      24. Figure 8.24
      25. Figure 8.25
      26. Figure 8.26
      27. Figure 8.27
      28. Figure 8.28
      29. Figure 8.29
      30. Figure 8.30
      31. Figure 8.31
      32. Figure 8.32
      33. Figure 8.33
      34. Figure 8.34
      35. Figure 8.35
  16. Chapter 9 Registration
    1. 9.1 Fusing Information
    2. 9.2 Registration Paradigms
      1. 9.2.1 Intra- and intermodal registration
      2. 9.2.2 Rigid and non-rigid registration
    3. 9.3 Merit Functions
    4. 9.4 Optimization Strategies
    5. 9.5 Some General Comments
    6. 9.6 Camera Calibration
    7. 9.7 Registration to Physical Space
      1. 9.7.1 Rigid registration using fiducial markers and surfaces
      2. 9.7.2 2D/3D registration
    8. 9.8 Evaluation of Registration Results
    9. 9.9 Practical Lessons
      1. 9.9.1 Registration of 2D images using cross-correlation
      2. 9.9.2 Computing joint histograms
      3. 9.9.3 Plotting the mutual information merit function
      4. 9.9.4 Chamfer matching
      5. 9.9.5 Optimization
      6. 9.9.6 The direct linear transform
      7. 9.9.7 Marker based registration
    10. 9.10 Summary and Further References
      1. Literature
    11. Figure 9.1
      1. Figure 9.1
      2. Figure 9.2
      3. Figure 9.3
      4. Figure 9.4
      5. Figure 9.5
      6. Figure 9.6
      7. Figure 9.7
      8. Figure 9.8
      9. Figure 9.9
      10. Figure 9.10
      11. Figure 9.11
      12. Figure 9.12
      13. Figure 9.13
      14. Figure 9.14
      15. Figure 9.15
      16. Figure 9.16
      17. Figure 9.17
      18. Figure 9.18
      19. Figure 9.19
      20. Figure 9.20
      21. Figure 9.21
      22. Figure 9.22
      23. Figure 9.23
      24. Figure 9.24
      25. Figure 9.25
      26. Figure 9.26
      27. Figure 9.27
    12. Table 9.1
      1. Table 9.1
      2. Table 9.2
  17. Chapter 10 CT Reconstruction
    1. 10.1 Introduction
    2. 10.2 Radon Transform
      1. 10.2.1 Attenuation
      2. 10.2.2 Definition of the Radon transform in the plane
      3. 10.2.3 Basic properties and examples
      4. 10.2.4 MATLAB® implementation
    3. 10.3 Algebraic Reconstruction
      1. 10.3.1 A system of linear equations
      2. 10.3.2 Computing the system matrix with MATLAB
      3. 10.3.3 How to solve the system of equations
    4. 10.4 Some Remarks on Fourier Transform and Filtering
    5. 10.5 Filtered Backprojection
      1. 10.5.1 Projection slice theorem
      2. 10.5.2 Filtered backprojection algorithm
    6. 10.6 Practical Lessons
      1. 10.6.1 Simple backprojection
      2. 10.6.2 Noise
      3. 10.6.3 Ring artifacts
      4. 10.6.4 Streak artifacts
      5. 10.6.5 Backprojection revisited – cone beam CT reconstruction
    7. 10.7 Summary and Further References
      1. Literature
    8. Figure 10.1
      1. Figure 10.1
      2. Figure 10.2
      3. Figure 10.3
      4. Figure 10.4
      5. Figure 10.5
      6. Figure 10.6
      7. Figure 10.7
      8. Figure 10.8
      9. Figure 10.9
      10. Figure 10.10
      11. Figure 10.11
      12. Figure 10.12
      13. Figure 10.13
      14. Figure 10.14
      15. Figure 10.15
      16. Figure 10.16
      17. Figure 10.17
      18. Figure 10.18
      19. Figure 10.19
      20. Figure 10.20
      21. Figure 10.21
      22. Figure 10.22
      23. Figure 10.23
      24. Figure 10.24
      25. Figure 10.25
      26. Figure 10.26
      27. Figure 10.27
  18. Chapter 11 A Tutorial on Image-Guided Therapy
    1. 11.1 A Hands-on Approach to Camera Calibration and Image-Guided Therapy
    2. 11.2 Transformations
      1. 11.2.1 Central projection
      2. 11.2.2 Homography
    3. 11.3 Camera Calibration
      1. 11.3.1 Pinhole camera model
      2. 11.3.2 Camera calibration with 3D object
      3. 11.3.3 Camera calibration with 2D object
    4. 11.4 Image-Guided Therapy, Introduction
    5. 11.5 Image-Guided Therapy, Navigation System
      1. 11.5.1 Setup and calibration
      2. 11.5.2 Procedure planning
      3. 11.5.3 Navigation
    6. 11.6 Image-Guided Therapy, Theory in Practice
      1. 11.6.1 Camera calibration
      2. 11.6.2 Tracking accuracy
      3. 11.6.3 Human computer interaction
      4. 11.6.4 Dynamic reference frame
      5. 11.6.5 Paired-Point rigid registration
      6. 11.6.6 Iterative-closest point
    7. 11.7 Summary and Further References
      1. Literature
    8. Figure 11.1
      1. Figure 11.1
      2. Figure 11.2
      3. Figure 11.3
      4. Figure 11.4
      5. Figure 11.5
      6. Figure 11.6
      7. Figure 11.7
      8. Figure 11.8
      9. Figure 11.9
      10. Figure 11.10
      11. Figure 11.11
      12. Figure 11.12
      13. Figure 11.13
      14. Figure 11.14
      15. Figure 11.15
      16. Figure 11.16
      17. Figure 11.17
    9. Table 11.1
      1. Table 11.1
  19. Chapter 12 A Selection of MATLAB® Commands
    1. 12.1 Control Structures and Operators
    2. 12.2 I/O and Data Structures
    3. 12.3 Mathematical Functions
    4. 12.4 Further References
  20. Glossary
  21. MATLAB sample scripts
  22. Epilogue
  23. Figure A.1
    1. Figure A.1
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