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Applied Medical Image Processing, 2nd Edition

Name: Applied Medical Image Processing, 2nd Edition
Author: Wolfgang Birkfellner
ISBN: 9781498759724

by Wolfgang Birkfellner

April 2016

Intermediate to advanced

455 pages

12h 41m

English

CRC Press

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Preliminaries
Dedication
Foreword
Preface to the First Edition
Preface to the Second Edition
User Guide
Literature
Acknowledgments
Chapter 1 A Few Basics of Medical Image Sources
1.1 Radiology1.2 The Electromagnetic Spectrum1.3 Basic X-Ray Physics1.3.1 The x-ray tube1.3.2 X-ray detectors1.4 Attenuation and Imaging1.5 Computed Tomography1.5.1 Basics of CT and scanner generations1.5.2 Imaging geometries and CT technologies1.5.3 Image artifacts in CT1.5.4 Safety aspects1.6 Magnetic Resonance Tomography1.6.1 Basic MRI physics1.6.1.1 Relaxation processes1.6.1.2 Spin-echo1.6.2 Tissue contrast1.6.3 Spatial localization1.6.3.1 Slice selection1.6.3.2 Frequency encoding1.6.3.3 Phase encoding1.6.4 Image artifacts in MR1.6.5 The elements of an MR system1.6.5.1 The main static magnetic field1.6.5.2 Gradient fields1.6.5.3 The RF system1.6.6 Safety aspects1.7 Ultrasound1.7.1 Some physical properties of sound1.7.2 Basic principles1.7.3 Image artifacts in B-Mode US imaging1.7.4 Doppler effect1.7.5 3D imaging1.7.6 Safety aspects1.8 Nuclear Medicine and Molecular Imaging1.8.1 Scintigraphy1.8.2 The γ camera1.8.3 SPECT and PET1.9 Other Imaging Techniques1.10 Radiation Protection and Dosimetry1.10.1 Terminology of dosimetry1.10.2 Radiation effects on tissue and organs1.10.3 Natural and man-made radiation exposure1.11 Summary and Further ReferencesLiteratureFigure 1.1Figure 1.2Figure 1.3Figure 1.4Figure 1.5Figure 1.6Figure 1.7Figure 1.8Figure 1.9Figure 1.10Figure 1.11Figure 1.12Figure 1.13Figure 1.14Figure 1.15Figure 1.16Figure 1.17Figure 1.18Figure 1.19Figure 1.20Figure 1.21Figure 1.22Figure 1.23Figure 1.24Figure 1.25Figure 1.26Figure 1.27Figure 1.28Table 1.1Table 1.2Table 1.3Table 1.4Table 1.5
Chapter 2 Image Processing in Clinical Practice
2.1 Application Examples2.2 Image Databases2.3 Intensity Operations2.4 Filter Operations2.5 Segmentation2.6 Spatial Transforms2.7 Rendering and Surface Models2.8 Registration2.9 CT Reconstruction2.10 SummaryFigure 2.1Figure 2.2Figure 2.3Figure 2.4Figure 2.5Figure 2.6Figure 2.7Figure 2.8Figure 2.9Figure 2.10Figure 2.11Figure 2.12Figure 2.13
Chapter 3 Image Representation
3.1 Pixels and Voxels3.1.1 Algebraic image operations3.2 Gray Scale and Color Representation3.2.1 Depth3.2.2 Color and look up tables3.3 Image File Formats3.4 Dicom3.5 Other Formats – Analyze 7.5, Nifti and Interfile3.6 Image Quality and the Signal-to-Noise Ratio3.7 Practical Lessons3.7.1 Image subtraction3.7.2 Opening and decoding a single DICOM file in MATLAB3.7.3 Opening a DICOM file using ImageJ and 3DSlicer3.7.3.1 A single slice in ImageJ3.7.3.2 A CT-volume in 3DSlicer3.7.4 Converting a color image to gray scale3.7.4.1 A note on good MATLAB programming habits3.7.5 Computing the SNR of x-ray images as a function of dose3.8 Summary and Further ReferencesLiteratureFigure 3.1Figure 3.2Figure 3.3Figure 3.4Figure 3.5Figure 3.6Figure 3.7Figure 3.8Figure 3.9Figure 3.10Figure 3.11Figure 3.12Figure 3.13Figure 3.14Figure 3.15Figure 3.16Figure 3.17Figure 3.18Figure 3.19Figure 3.20Figure 3.21Figure 3.22Figure 3.23

Chapter 4 Operations in Intensity Space
4.1 The Intensity Transform Function and The Dynamic Range4.2 Windowing4.3 Histograms and Histogram Operations4.4 Dithering and Depth4.5 Practical Lessons4.5.1 Linear adjustment of image depth range4.5.1.1 A note on good MATLAB® programming habits4.5.2 Composing a color image from grayscale images4.5.3 Improving visibility of low-contrast detail – taking the logarithm4.5.4 Modelling a general nonlinear transfer function – the Sigmoid4.5.5 Histograms and histogram operations4.5.6 Automatic optimization of image contrast using the histogram4.5.7 Intensity operations using ImageJ and 3DSlicer4.6 Summary and Further ReferencesLiteratureFigure 4.1Figure 4.2Figure 4.3Figure 4.4Figure 4.5Figure 4.6Figure 4.7Figure 4.8Figure 4.9Figure 4.10Figure 4.11Figure 4.12Figure 4.13Figure 4.14Figure 4.15Figure 4.16Figure 4.17Figure 4.18Figure 4.19Figure 4.20Figure 4.21Figure 4.22Table 4.1
Chapter 5 Filtering and Transformations
5.1 The Filtering Operation5.1.1 Kernel based smoothing and sharpening operations5.1.2 Differentiation and edge detection5.1.3 Helpful non-linear filters5.2 The Fourier Transform5.2.1 Basic linear algebra and series expansions5.2.2 Waves - a special orthonormal system5.2.3 Some important properties of the Fourier transform5.2.4 Image processing in the frequency domain5.2.5 Modelling properties of imaging systems - the PSF and the MTF5.3 Other Transforms5.3.1 The Hough transform5.3.2 The distance transform5.4 Practical Lessons5.4.1 Kernel – based low pass and high pass filtering5.4.2 Basic filtering operations in ImageJ5.4.3 Numerical differentiation5.4.3.1 A note on good MATLAB® programming habits5.4.4 Unsharp masking5.4.5 The median filter5.4.6 Some properties of the Fourier-transform5.4.6.1 Spectra of simple functions in k-space5.4.6.2 More complex functions5.4.6.3 Convolution of simple functions5.4.6.4 Differentiation in k-space5.4.7 Frequency filtering in Fourier-space on images5.4.8 Applied convolution – PSF and the MTF5.4.9 Determination of system resolution of an Anger-camera using a point source5.4.10 The Hough transform5.4.11 The distance transform5.5 Summary and Further ReferencesLiteratureFigure 5.1Figure 5.2Figure 5.3Figure 5.4Figure 5.5Figure 5.6Figure 5.7Figure 5.8Figure 5.9Figure 5.10Figure 5.11Figure 5.12Figure 5.13Figure 5.14Figure 5.15Figure 5.16Figure 5.17Figure 5.18Figure 5.19Figure 5.20Figure 5.21Figure 5.22Figure 5.23Figure 5.24Figure 5.25Figure 5.26Figure 5.27Figure 5.28Figure 5.29Figure 5.30Figure 5.31Figure 5.32Figure 5.33Figure 5.34Figure 5.35Figure 5.36Figure 5.37Figure 5.38Figure 5.39Figure 5.40Figure 5.41Figure 5.42Figure 5.43Figure 5.44Figure 5.45Figure 5.46Figure 5.47Figure 5.48Figure 5.49
Chapter 6 Segmentation
6.1 The Segmentation Problem6.2 ROI Definition and Centroids6.3 Thresholding6.4 Region Growing6.5 More Sophisticated Segmentation Methods6.5.1 ITK SNAP – a powerful implementation of a segmentation algorithm at work6.6 Morphological Operations6.7 Evaluation of Segmentation Results6.8 Practical Lessons6.8.1 Count rate evaluation by ROI selection6.8.2 Region definition by global thresholding6.8.3 Region growing6.8.4 Region growing in 3D6.8.5 A very simple snake-type example6.8.6 Erosion and dilation6.8.7 Hausdorff-distances and Dice-coefficients6.8.8 Improving segmentation results by filtering6.8.8.1 Better thresholding6.8.8.2 Preprocessing and active contours6.9 Summary and Further ReferencesLiteratureFigure 6.1Figure 6.2Figure 6.3Figure 6.4Figure 6.5Figure 6.6Figure 6.7Figure 6.8Figure 6.9Figure 6.10Figure 6.11Figure 6.12Figure 6.13Figure 6.14Figure 6.15Figure 6.16Figure 6.17Figure 6.18Figure 6.19Figure 6.20Figure 6.21Figure 6.22Figure 6.23Figure 6.24Figure 6.25Figure 6.26Figure 6.27Figure 6.28Figure 6.29Figure 6.30
Chapter 7 Spatial Transforms
7.1 Discretization – Resolution and Artifacts7.2 Interpolation and Volume Regularization7.3 Translation and Rotation7.3.1 Rotation in 2D - some properties of the rotation matrix7.3.2 Rotation and translation in 3D7.3.3 A special case - the principal axis transform7.3.4 The quaternion representation of rotations7.4 Reformatting7.5 Tracking and Image-Guided Therapy7.6 Practical Lessons7.6.1 Spatial image transforms in 2D7.6.2 Two simple interpolation examples7.6.3 A special case – the PCA on binary images7.6.4 A geometric reformatting example – conic sections7.6.5 A reformatting example on a small volume data set7.6.6 Convolution revisited7.7 Summary and Further ReferencesLiteratureFigure 7.1Figure 7.2Figure 7.3Figure 7.4Figure 7.5Figure 7.6Figure 7.7Figure 7.8Figure 7.9Figure 7.10Figure 7.11Figure 7.12Figure 7.13Figure 7.14Figure 7.15Figure 7.16Figure 7.17Figure 7.18Figure 7.19Figure 7.20Figure 7.21
Chapter 8 Rendering and Surface Models
8.1 Visualization8.2 Orthogonal and Perspective Projection, And the Viewpoint8.3 Raycasting8.3.1 MIP, DRRs and volume rendering8.3.2 Other rendering techniques8.4 Surface-Based Rendering8.4.1 Surface extraction, file formats for surfaces, shading and textures8.4.2 Shading models8.4.3 A special application – virtual endoscopy8.5 Practical Lessons8.5.1 A perspective example8.5.2 Simple orthogonal raycasting8.5.3 Viewpoint transforms and splat rendering8.5.4 Volume rendering using color coding8.5.5 A simple surface rendering - depth shading8.5.6 Rendering of voxel surfaces8.5.7 A rendering example using 3DSlicer8.5.8 Extracting a surface using the cuberille algorithm8.5.9 A demonstration of shading effects8.6 Summary and Further ReferencesLiteratureFigure 8.1Figure 8.2Figure 8.3Figure 8.4Figure 8.5Figure 8.6Figure 8.7Figure 8.8Figure 8.9Figure 8.10Figure 8.11Figure 8.12Figure 8.13Figure 8.14Figure 8.15Figure 8.16Figure 8.17Figure 8.18Figure 8.19Figure 8.20Figure 8.21Figure 8.22Figure 8.23Figure 8.24Figure 8.25Figure 8.26Figure 8.27Figure 8.28Figure 8.29Figure 8.30Figure 8.31Figure 8.32Figure 8.33Figure 8.34Figure 8.35
Chapter 9 Registration
9.1 Fusing Information9.2 Registration Paradigms9.2.1 Intra- and intermodal registration9.2.2 Rigid and non-rigid registration9.3 Merit Functions9.4 Optimization Strategies9.5 Some General Comments9.6 Camera Calibration9.7 Registration to Physical Space9.7.1 Rigid registration using fiducial markers and surfaces9.7.2 2D/3D registration9.8 Evaluation of Registration Results9.9 Practical Lessons9.9.1 Registration of 2D images using cross-correlation9.9.2 Computing joint histograms9.9.3 Plotting the mutual information merit function9.9.4 Chamfer matching9.9.5 Optimization9.9.6 The direct linear transform9.9.7 Marker based registration9.10 Summary and Further ReferencesLiteratureFigure 9.1Figure 9.2Figure 9.3Figure 9.4Figure 9.5Figure 9.6Figure 9.7Figure 9.8Figure 9.9Figure 9.10Figure 9.11Figure 9.12Figure 9.13Figure 9.14Figure 9.15Figure 9.16Figure 9.17Figure 9.18Figure 9.19Figure 9.20Figure 9.21Figure 9.22Figure 9.23Figure 9.24Figure 9.25Figure 9.26Figure 9.27Table 9.1Table 9.2
Chapter 10 CT Reconstruction
10.1 Introduction10.2 Radon Transform10.2.1 Attenuation10.2.2 Definition of the Radon transform in the plane10.2.3 Basic properties and examples10.2.4 MATLAB® implementation10.3 Algebraic Reconstruction10.3.1 A system of linear equations10.3.2 Computing the system matrix with MATLAB10.3.3 How to solve the system of equations10.4 Some Remarks on Fourier Transform and Filtering10.5 Filtered Backprojection10.5.1 Projection slice theorem10.5.2 Filtered backprojection algorithm10.6 Practical Lessons10.6.1 Simple backprojection10.6.2 Noise10.6.3 Ring artifacts10.6.4 Streak artifacts10.6.5 Backprojection revisited – cone beam CT reconstruction10.7 Summary and Further ReferencesLiteratureFigure 10.1Figure 10.2Figure 10.3Figure 10.4Figure 10.5Figure 10.6Figure 10.7Figure 10.8Figure 10.9Figure 10.10Figure 10.11Figure 10.12Figure 10.13Figure 10.14Figure 10.15Figure 10.16Figure 10.17Figure 10.18Figure 10.19Figure 10.20Figure 10.21Figure 10.22Figure 10.23Figure 10.24Figure 10.25Figure 10.26Figure 10.27
Chapter 11 A Tutorial on Image-Guided Therapy
11.1 A Hands-on Approach to Camera Calibration and Image-Guided Therapy11.2 Transformations11.2.1 Central projection11.2.2 Homography11.3 Camera Calibration11.3.1 Pinhole camera model11.3.2 Camera calibration with 3D object11.3.3 Camera calibration with 2D object11.4 Image-Guided Therapy, Introduction11.5 Image-Guided Therapy, Navigation System11.5.1 Setup and calibration11.5.2 Procedure planning11.5.3 Navigation11.6 Image-Guided Therapy, Theory in Practice11.6.1 Camera calibration11.6.2 Tracking accuracy11.6.3 Human computer interaction11.6.4 Dynamic reference frame11.6.5 Paired-Point rigid registration11.6.6 Iterative-closest point11.7 Summary and Further ReferencesLiteratureFigure 11.1Figure 11.2Figure 11.3Figure 11.4Figure 11.5Figure 11.6Figure 11.7Figure 11.8Figure 11.9Figure 11.10Figure 11.11Figure 11.12Figure 11.13Figure 11.14Figure 11.15Figure 11.16Figure 11.17Table 11.1
Chapter 12 A Selection of MATLAB® Commands
12.1 Control Structures and Operators12.2 I/O and Data Structures12.3 Mathematical Functions12.4 Further References
Glossary
MATLAB sample scripts
Epilogue
Figure A.1

Content preview from Applied Medical Image Processing, 2nd Edition

Epilogue

FIGURE A.1:

Figure showing compiling the displaypgm.cpp code from the LessonData folder should give you something like this.

Compiling the displaypgm.cpp code from the LessonData folder should give you something like this.

Once you have reached this chapter, you have made it. You made acquaintance with the most important basic methods and algorithms in medical image processing. If you want to go further, you may find a plethora of advanced textbooks and research articles to guide you. However, one problem remains. MATLAB is a fantastic tool for experimenting and teaching, but it is not necessarily the right environment for advanced programming, especially when it comes to managing the memory requirements of really large datasets. Here begins the ...

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Applied Medical Image Processing, 2nd Edition

by Wolfgang Birkfellner

Epilogue

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