Table of contents for A practical approach to medical image processing / author, Elizabeth Berry.

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CONTENTS
PREFACE
ACKNOWLEDGMENTS
ON THE CD
INSTALLATION INSTRUCTIONS FOR IMAGEJ
1. IMAGE PROCESSING BASICS
1.1 INTRODUCTION
1.1.1 Learning objectives
1.1.2 Example of an image used in this chapter
1.2 DEFINITION OF IMAGE PROCESSING
1.2.1 The digital image
1.2.2 Image resolution
1.2.2.1 Greyscale resolution
1.2.2.2 Spatial resolution
1.2.3 Image data types
1.3 INTRODUCTION TO IMAGEJ
1.3.1 Activity: Starting and closing ImageJ
1.3.2 Activity: Loading and saving an image with ImageJ
1.3.3 Activity: 16-bit and 8-bit images
1.3.4 Activity: Spatial resolution
1.4 GREYSCALE IMAGE PROCESSING BASICS
1.4.1 Color look up tables (LUT)
1.4.1.1 Activity: Look up tables in ImageJ
1.4.2 Image contrast
1.4.3 The image histogram
1.4.3.1 Image contrast and the histogram
1.5.3.2 Actual dynamic range
1.4.3.3 Histogram operations
1.4.4 Pixel statistics
1.4.4.1 Profile statistics
1.4.4.2 Region of interest statistics
1.5 SPATIAL IMAGE PROCESSING BASICS
1.5.1 Image Size
1.5.2 Neighbors and connections
1.5.3 Scaling and rotation
1.5.3.1 Activity: Image rotation in ImageJ
1.5.4 Interpolation
1.6 THE FIVE CLASSES OF IMAGE PROCESSING
1.6.1 Image Enhancement
1.6.2 Image Restoration
1.6.3 Image Analysis
1.6.4 Image Compression
1.6.5 Image Synthesis
1.7 GOOD PRACTICE CONSIDERATIONS
1.8 CHAPTER SUMMARY
1.9 FEEDBACK ON THE SELF-ASSESSMENT QUESTIONS
2. SEGMENTATION AND CLASSIFICATION
2.1 INTRODUCTION
2.1.1 Learning objectives
2.1.2 Example of an image used in this chapter
2.2 SEGMENTATION
2.2.1 Approaches to segmentation
2.2.2 Manual segmentation methods
2.2.2.1 Activity: Manual segmentation in ImageJ
2.2.3 Semi-automatic segmentation
2.2.3.1 Thresholding
2.2.3.2 Activity: Thresholding in ImageJ
2.2.3.3 Region growing
2.2.3.4 Active contours and snakes
2.2.3.5 Mathematical morphology
2.3 CLASSIFICATION
2.3.1 Multispectral classification
2.3.2 Supervised vs. unsupervised classification methods
2.3.3 Medical imaging applications for classification
2.3.4 An example of supervised classification
2.4 GOOD PRACTICE CONSIDERATIONS
2.5 CHAPTER SUMMARY
2.6 FEEDBACK ON THE SELF-ASSESSMENT QUESTIONS
REFERENCES
3. SPATIAL DOMAIN FILTERING
3.1 INTRODUCTION
3.1.1 Learning objectives
3.1.2 Example of an image used in this chapter
3.2 SPATIAL FILTERING OPERATIONS
3.2.1 Rank filtering
3.2.1.1 Activity: Median filtering in ImageJ
3.2.1.2 Effect of neighborhood size and shape
3.2.2 Convolution filtering
3.2.2.1 Activity: Convolution filtering in ImageJ
3.2.2.3 Effect of kernel size
3.2.3 Hybrid Filtering
3.3 ADAPTIVE FILTERING
3.4 GOOD PRACTICE CONSIDERATIONS
3.5 CHAPTER SUMMARY
3.6 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
REFERENCES
4. FREQUENCY DOMAIN FILTERING
4.1 INTRODUCTION
4.1.1 Learning objectives
4.1.2 Example of an image used in this chapter
4.1.3 Convolution filtering: a reminder
4.2 THE SPATIAL DOMAIN AND THE FREQUENCY DOMAIN
4.3 FREQUENCY DOMAIN FILTERING
4.3.1 Advantages of filtering in the frequency domain
4.3.2 The FFT and the spectrum
4.3.3 Application of the filter to the spectrum
4.3.3.1 Activity: Low Pass Frequency Domain Filtering using ImageJ
4.3.3.2 Activity: High Pass Frequency Domain Filtering using ImageJ
4.3.3.3 Activity: Band Pass Frequency Domain Filtering using ImageJ
4.3.4 Why filtering in the frequency domain is the same as convolution filtering in the spatial domain
4.3.4.1 Activity: Comparison of frequency domain and convolution filtering using ImageJ
4.3.5 Use of the word convolution
4.4 GOOD PRACTICE CONSIDERATIONS
4.5 CHAPTER SUMMARY
4.6 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
5. IMAGE ANALYSIS OPERATIONS
5.1 INTRODUCTION
5.1.1 Learning objectives
5.1.2 Example of an image used in this chapter
5.2 IMAGE ARITHMETIC
5.2.1 Operations on data in one image
5.2.1.1 Typical operations
5.2.1.2 Data type
5.2.1.3 Activity: Being prepared for negative numbers when performing image arithmetic in ImageJ
5.2.1.4 Activity: Using the ImageJ Math menu
5.2.2 Operations using data from two images
5.2.2.1 Typical operations
5.2.2.3 Activity: The ImageJ Image Calculator
5.2.3 Spatial and grey level calibration
5.2.3.1 Spatial calibration
5.2.3.2 Grey level calibration
5.3 BINARY IMAGE OPERATIONS
5.3.1 Logical operators
5.3.1.1 Activity: Using logical operations in ImageJ
5.3.2 Morphological operations
5.3.2.1 Activity: Using morphological operations in ImageJ
5.4 LOGICAL AND MORPHOLOGICAL OPERATIONS ON GREY SCALE IMAGES
5.5 GOOD PRACTICE CONSIDERATIONS
5.6 CHAPTER SUMMARY
5.7 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
6. IMAGE DATA FORMATS AND IMAGE COMPRESSION
6.1 INTRODUCTION
6.1.1 Learning objectives
6.1.2 Example of an image used in this chapter
6.2 IMAGE DATA FORMATS
6.2.1 The need for image data formats
6.2.2 Image header
6.2.2.1 Activity: Importing an image from a text file into ImageJ
6.2.2.2 Activity: Importing an image from a raw data file into ImageJ
6.2.3 General purpose image data formats
6.2.4 DICOM®
6.2.4.1 Activity: The wealth of information in a DICOM file
6.2.5 The effect of missing information
6.2.5.1 Activity: Importing an image with unknown format using ImageJ
6.3 IMAGE COMPRESSION
6.3.1 Compression ratio
6.3.2 Lossy vs. lossless compression
6.3.3 Run length encoding (RLE)
6.3.4 Huffman coding
6.3.5 JPEG compression
6.3.5.1 Activity: JPEG compression in ImageJ
6.4 GOOD PRACTICE CONSIDERATIONS
6.5 CHAPTER SUMMARY
6.6 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
REFERENCES
7. IMAGE RESTORATION
7.1 INTRODUCTION
7.1.1 Learning objectives
7.1.2 Example of an image used in this chapter
7.2 BLURRING ARISING FROM THE IMAGING SYSTEM ITSELF
7.2.1 Point spread function (PSF)
7.2.2 Deconvolution
7.2.2.1 Activity: Effect of small differences in the PSF on the deconvolution result
7.2.3 Modulation transfer function (MTF)
7.3 GEOMETRICAL DISTORTION
7.3.1 Methods for correction of geometrical distortion
7.4 GREY LEVEL INHOMOGENEITY
7.4.1 Methods for correction of grey level inhomogeneity
7.4.1.1 Correction using image of a uniform object
7.4.1.2 Correction derived from acquired subject data
7.4.1.3 Activity: Simple inhomegeneity correction using ImageJ
7.5 GOOD PRACTICE CONSIDERATIONS
7.6 CHAPTER SUMMARY
7.7 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
REFERENCES
8. IMAGE REGISTRATION
8.1 INTRODUCTION
8.1.1 Learning objectives
8.1.2 Example of an image used in this chapter
8.2 IMAGE REGISTRATION
8.2.1 Overview of the four steps of image registration
8.2.2 Step 1. Feature extraction
8.2.3 Step 2. Pairing
8.2.3.1 Identifying correspondences where fiducials or anatomical landmarks are used as features
8.2.3.2 Identifying correspondences where the pixel or related values are used as features 
8.2.4 Step 3. Calculation of transformation
8.2.4.1 Types of geometrical transformation
8.2.4.2 Calculation of the transformation where fiducials or anatomical landmarks are used as features
8.2.4.3 Calculation of the transformation where pixel or related values are used as features
8.2.5 Step 4. Application of transformation
8.2.6 Factors that can cause errors in the registration process
8.2.7 Activity: Image registration with anatomical landmarks using ImageJ
8.3 DIMENSIONALITY AND NUMBER OF MODALITIES
8.4 VISUALIZATION OF REGISTERED IMAGES
8.5 APPLICATIONS
8.5.1 Assessment of disease progression or growth using temporal series
8.5.2 Combination of structural and functional information from different modalities
8.5.3 Creation of atlases or templates representing the typical appearance in health or disease
8.5.4 Preparation for arithmetical or statistical operations, such as subtraction in contrast enhanced data or statistical parametric mapping
8.5.5 Generation of a ¿roadmap¿ for invasive procedures and image guided surgery
8.5.6 But image registration may not be necessary ¿
8.6 GOOD PRACTICE CONSIDERATIONS
8.7 CHAPTER SUMMARY
8.8 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
REFERENCES
9. VISUALIZATION AND 3D METHODS
9.1 INTRODUCTION
9.1.1 Learning objectives
9.1.2 Example of an image used in this chapter
9.2 TAXONOMY FOR VISUALIZATION
9.3 SCENE-BASED VISUALIZATION
9.3.1 Slice mode
9.3.1.1 Activity: Slice mode visualization in ImageJ
9.3.1.2 Activity: Slice mode rendering using ImageJ
9.3.2 Volume mode
9.3.2.1 Maximum intensity projection (MIP)
9.3.2.2 Surface rendering
9.3.2.3 Volume rendering
9.3.2.4 Activity: 3D rendering using ImageJ
9.4 OBJECT-BASED VISUALIZATION
9.5 OTHER VISUALIZATION METHODS
9.5.1 Parametric displays
9.5.2 Virtual environments
9.5.3 Physical and hybrid representations
9.6 GOOD PRACTICE CONSIDERATIONS
9.7 CHAPTER SUMMARY
9.8 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
REFERENCES
10. GOOD PRACTICE
10.1 INTRODUCTION
10.1.1 Learning objectives
10.2 SCIENTIFIC RIGOR
10.2.1 Protocols and workflows
10.2.2 Avoid the arbitrary or subjective
10.2.3 Recording and reporting
10.2.4 Data integrity and image provenance
10.3 ETHICAL PRACTICE
10.4 HUMAN FACTORS
10.4.1 Variability
10.4.2 Color vision deficiency and pseudocolor displays
10.4.3 Color vision deficiency and stereograms viewed using red and green glasses
10.5 INFORMATION TECHNOLOGY
10.5.1 Software tools
10.5.2 Images on the web
10.6 EVALUATION OF THE PERFORMANCE OF IMAGE PROCESSING METHODS
10.6.1 Qualitative evaluation
10.6.1.1 Qualitative evaluation of segmentation
10.6.1.2 Qualitative evaluation of registration
10.6.1.3 Qualitative evaluation of visualization
10.6.2 Quantitative evaluation
10.6.2.1 Quantitative evaluation of segmentation
10.6.2.2 Quantitative evaluation of registration
10.6.2.3 Quantitative evaluation of visualization
10.6.3 Health Technology Assessment
10.8 CHAPTER SUMMARY
10.9 FEEDBACK ON SELF-ASSESSMENT QUESTIONS
REFERENCES
11. CASE STUDIES
11.1 INTRODUCTION
11.1.1 Learning objectives
11.2 IMAGE ENHANCEMENT
11.2.1 Questions on the image enhancement article
11.2.2 Activities: Image enhancement and the digital frame of reference using ImageJ
11.2.3 Feedback on the image enhancement case study
11.3 SEGMENTATION
11.3.1 Questions on the segmentation article
11.3.2 Activity: Manual segmentation using ImageJ
11.3.3 Activity: Comparison with a binary reference standard image using ImageJ
11.3.4 Activity: Reproducibility of manual segmentation
11.3.5 Activity: Semi-automated segmentation using ImageJ
11.3.6 Questions about the ImageJ activities
11.3.5 Feedback on the segmentation case study
11.4 IMAGE COMPRESSION
11.4.1 Questions on the image compressionarticle
11.4.2 Activity: File size vs. Real size
11.4.3 Activity: Lossy jpeg artifacts
11.4.4 Activity: Effect of repeated jpeg saves
11.4.5 Activity: Lossless PNG format
11.4.6 Activity: Compression ratios differ for different modalities
11.4.7 Feedback on the image compression case study
11.5 IMAGE REGISTRATION
11.5.2 Questions on the image registration article
11.5.3 Activity: Image registration using mutual information with ImageJ and TurboReg
11.5.4 Activity: The joint histogram using ImageJ
11.5.5 Activity: Affine transformation using ImageJ and TurboReg
11.5.6 Activity: Preliminary alignment using landmarks using ImageJ and TurboReg
11.5.7 Further questions on the article
11.5.8 Feedback on the image registration case study
11.6 VISUALIZATION
11.6.1 Questions on the visualization article
11.6.2 Activity: Volume rendering using ImageJ and VolumeJ
11.6.3 Activity: Generating an anaglyph using ImageJ and VolumeJ
11.6.4 Activity: Volume rendering movies using ImageJ and VolumeJ
11.6.5 One last question on the article
11.6.6 Feedback on the visualization case study
11.7 CHAPTER SUMMARY
REFERENCES
12. FOR INSTRUCTORS
12.1 INTRODUCTION
12.2 WRITING MACROS AND PLUGINS FOR IMAGEJ
12.2.1 Macros
12.2.2 Plugins
12.3 ARTICLE-BASED CASE STUDIES
12.3.1 Choosing a case study article
12.3.2 Alternative articles for case studies
12.3.3 Non-medical imaging case studies
12.4 EXTENSIONS TO MATERIAL IN PRECEDING CHAPTERS
12.4.1 Basic Image Processing
12.4.2 Segmentation and classification
12.4.3 Spatial domain filtering
12.4.3.1 Activity: The underlying principle of adaptive filtering
12.4.4 Frequency domain filtering
12.4.5 Image analysis operations
12.4.6 Image data formats and image compression
12.4.7 Image restoration
12.4.8 Image Registration
12.4.8.1 Activity: Image registration by chamfer matching in ImageJ
12.4.9 Visualization
12.4.10 Good practice and evaluation
12.5 PUBLICLY AVAILABLE DATA
12.6 CHAPTER SUMMARY
REFERENCES

Library of Congress Subject Headings for this publication:

Imaging systems in medicine.
Image analysis -- Data processing.
Image processing -- Digital techniques.
Image Processing, Computer-Assisted -- methods.
Diagnostic Imaging -- methods.