- •Biological and Medical Physics, Biomedical Engineering
- •Medical Image Processing
- •Preface
- •Contents
- •Contributors
- •1.1 Medical Image Processing
- •1.2 Techniques
- •1.3 Applications
- •1.4 The Contribution of This Book
- •References
- •2.1 Introduction
- •2.2 MATLAB and DIPimage
- •2.2.1 The Basics
- •2.2.2 Interactive Examination of an Image
- •2.2.3 Filtering and Measuring
- •2.2.4 Scripting
- •2.3 Cervical Cancer and the Pap Smear
- •2.4 An Interactive, Partial History of Automated Cervical Cytology
- •2.5 The Future of Automated Cytology
- •2.6 Conclusions
- •References
- •3.1 The Need for Seed-Driven Segmentation
- •3.1.1 Image Analysis and Computer Vision
- •3.1.2 Objects Are Semantically Consistent
- •3.1.3 A Separation of Powers
- •3.1.4 Desirable Properties of Seeded Segmentation Methods
- •3.2 A Review of Segmentation Techniques
- •3.2.1 Pixel Selection
- •3.2.2 Contour Tracking
- •3.2.3 Statistical Methods
- •3.2.4 Continuous Optimization Methods
- •3.2.4.1 Active Contours
- •3.2.4.2 Level Sets
- •3.2.4.3 Geodesic Active Contours
- •3.2.5 Graph-Based Methods
- •3.2.5.1 Graph Cuts
- •3.2.5.2 Random Walkers
- •3.2.5.3 Watershed
- •3.2.6 Generic Models for Segmentation
- •3.2.6.1 Continuous Models
- •3.2.6.2 Hierarchical Models
- •3.2.6.3 Combinations
- •3.3 A Unifying Framework for Discrete Seeded Segmentation
- •3.3.1 Discrete Optimization
- •3.3.2 A Unifying Framework
- •3.3.3 Power Watershed
- •3.4 Globally Optimum Continuous Segmentation Methods
- •3.4.1 Dealing with Noise and Artifacts
- •3.4.2 Globally Optimal Geodesic Active Contour
- •3.4.3 Maximal Continuous Flows and Total Variation
- •3.5 Comparison and Discussion
- •3.6 Conclusion and Future Work
- •References
- •4.1 Introduction
- •4.2 Deformable Models
- •4.2.1 Point-Based Snake
- •4.2.1.1 User Constraint Energy
- •4.2.1.2 Snake Optimization Method
- •4.2.2 Parametric Deformable Models
- •4.2.3 Geometric Deformable Models (Active Contours)
- •4.2.3.1 Curve Evolution
- •4.2.3.2 Level Set Concept
- •4.2.3.3 Geodesic Active Contour
- •4.2.3.4 Chan–Vese Deformable Model
- •4.3 Comparison of Deformable Models
- •4.4 Applications
- •4.4.1 Bone Surface Extraction from Ultrasound
- •4.4.2 Spinal Cord Segmentation
- •4.4.2.1 Spinal Cord Measurements
- •4.4.2.2 Segmentation Using Geodesic Active Contour
- •4.5 Conclusion
- •References
- •5.1 Introduction
- •5.2 Imaging Body Fat
- •5.3 Image Artifacts and Their Impact on Segmentation
- •5.3.1 Partial Volume Effect
- •5.3.2 Intensity Inhomogeneities
- •5.4 Overview of Segmentation Techniques Used to Isolate Fat
- •5.4.1 Thresholding
- •5.4.2 Selecting the Optimum Threshold
- •5.4.3 Gaussian Mixture Model
- •5.4.4 Region Growing
- •5.4.5 Adaptive Thresholding
- •5.4.6 Segmentation Using Overlapping Mosaics
- •5.6 Conclusions
- •References
- •6.1 Introduction
- •6.2 Clinical Context
- •6.3 Vessel Segmentation
- •6.3.1 Survey of Vessel Segmentation Methods
- •6.3.1.1 General Overview
- •6.3.1.2 Region-Growing Methods
- •6.3.1.3 Differential Analysis
- •6.3.1.4 Model-Based Filtering
- •6.3.1.5 Deformable Models
- •6.3.1.6 Statistical Approaches
- •6.3.1.7 Path Finding
- •6.3.1.8 Tracking Methods
- •6.3.1.9 Mathematical Morphology Methods
- •6.3.1.10 Hybrid Methods
- •6.4 Vessel Modeling
- •6.4.1 Motivation
- •6.4.1.1 Context
- •6.4.1.2 Usefulness
- •6.4.2 Deterministic Atlases
- •6.4.2.1 Pioneering Works
- •6.4.2.2 Graph-Based and Geometric Atlases
- •6.4.3 Statistical Atlases
- •6.4.3.1 Anatomical Variability Handling
- •6.4.3.2 Recent Works
- •References
- •7.1 Introduction
- •7.2 Linear Structure Detection Methods
- •7.3.1 CCM for Imaging Diabetic Peripheral Neuropathy
- •7.3.2 CCM Image Characteristics and Noise Artifacts
- •7.4.1 Foreground and Background Adaptive Models
- •7.4.2 Local Orientation and Parameter Estimation
- •7.4.3 Separation of Nerve Fiber and Background Responses
- •7.4.4 Postprocessing the Enhanced-Contrast Image
- •7.5 Quantitative Analysis and Evaluation of Linear Structure Detection Methods
- •7.5.1 Methodology of Evaluation
- •7.5.2 Database and Experiment Setup
- •7.5.3 Nerve Fiber Detection Comparison Results
- •7.5.4 Evaluation of Clinical Utility
- •7.6 Conclusion
- •References
- •8.1 Introduction
- •8.2 Methods
- •8.2.1 Linear Feature Detection by MDNMS
- •8.2.2 Check Intensities Within 1D Window
- •8.2.3 Finding Features Next to Each Other
- •8.2.4 Gap Linking for Linear Features
- •8.2.5 Quantifying Branching Structures
- •8.3 Linear Feature Detection on GPUs
- •8.3.1 Overview of GPUs and Execution Models
- •8.3.2 Linear Feature Detection Performance Analysis
- •8.3.3 Parallel MDNMS on GPUs
- •8.3.5 Results for GPU Linear Feature Detection
- •8.4.1 Architecture and Implementation
- •8.4.2 HCA-Vision Features
- •8.4.3 Linear Feature Detection and Analysis Results
- •8.5 Selected Applications
- •8.5.1 Neurite Tracing for Drug Discovery and Functional Genomics
- •8.5.2 Using Linear Features to Quantify Astrocyte Morphology
- •8.5.3 Separating Adjacent Bacteria Under Phase Contrast Microscopy
- •8.6 Perspectives and Conclusions
- •References
- •9.1 Introduction
- •9.2 Bone Imaging Modalities
- •9.2.1 X-Ray Projection Imaging
- •9.2.2 Computed Tomography
- •9.2.3 Magnetic Resonance Imaging
- •9.2.4 Ultrasound Imaging
- •9.3 Quantifying the Microarchitecture of Trabecular Bone
- •9.3.1 Bone Morphometric Quantities
- •9.3.2 Texture Analysis
- •9.3.3 Frequency-Domain Methods
- •9.3.4 Use of Fractal Dimension Estimators for Texture Analysis
- •9.3.4.1 Frequency-Domain Estimation of the Fractal Dimension
- •9.3.4.2 Lacunarity
- •9.3.4.3 Lacunarity Parameters
- •9.3.5 Computer Modeling of Biomechanical Properties
- •9.4 Trends in Imaging of Bone
- •References
- •10.1 Introduction
- •10.1.1 Adolescent Idiopathic Scoliosis
- •10.2 Imaging Modalities Used for Spinal Deformity Assessment
- •10.2.1 Current Clinical Practice: The Cobb Angle
- •10.2.2 An Alternative: The Ferguson Angle
- •10.3 Image Processing Methods
- •10.3.1 Previous Studies
- •10.3.2 Discrete and Continuum Functions for Spinal Curvature
- •10.3.3 Tortuosity
- •10.4 Assessment of Image Processing Methods
- •10.4.1 Patient Dataset and Image Processing
- •10.4.2 Results and Discussion
- •10.5 Summary
- •References
- •11.1 Introduction
- •11.2 Retinal Imaging
- •11.2.1 Features of a Retinal Image
- •11.2.2 The Reason for Automated Retinal Analysis
- •11.2.3 Acquisition of Retinal Images
- •11.3 Preprocessing of Retinal Images
- •11.4 Lesion Based Detection
- •11.4.1 Matched Filtering for Blood Vessel Segmentation
- •11.4.2 Morphological Operators in Retinal Imaging
- •11.5 Global Analysis of Retinal Vessel Patterns
- •11.6 Conclusion
- •References
- •12.1 Introduction
- •12.1.1 The Progression of Diabetic Retinopathy
- •12.2 Automated Detection of Diabetic Retinopathy
- •12.2.1 Automated Detection of Microaneurysms
- •12.3 Image Databases
- •12.4 Tortuosity
- •12.4.1 Tortuosity Metrics
- •12.5 Tracing Retinal Vessels
- •12.5.1 NeuronJ
- •12.5.2 Other Software Packages
- •12.6 Experimental Results and Discussion
- •12.7 Summary and Future Work
- •References
- •13.1 Introduction
- •13.2 Volumetric Image Visualization Methods
- •13.2.1 Multiplanar Reformation (2D slicing)
- •13.2.2 Surface-Based Rendering
- •13.2.3 Volumetric Rendering
- •13.3 Volume Rendering Principles
- •13.3.1 Optical Models
- •13.3.2 Color and Opacity Mapping
- •13.3.2.2 Transfer Function
- •13.3.3 Composition
- •13.3.4 Volume Illumination and Illustration
- •13.4 Software-Based Raycasting
- •13.4.1 Applications and Improvements
- •13.5 Splatting Algorithms
- •13.5.1 Performance Analysis
- •13.5.2 Applications and Improvements
- •13.6 Shell Rendering
- •13.6.1 Application and Improvements
- •13.7 Texture Mapping
- •13.7.1 Performance Analysis
- •13.7.2 Applications
- •13.7.3 Improvements
- •13.7.3.1 Shading Inclusion
- •13.7.3.2 Empty Space Skipping
- •13.8 Discussion and Outlook
- •References
- •14.1 Introduction
- •14.1.1 Magnetic Resonance Imaging
- •14.1.2 Compressed Sensing
- •14.1.3 The Role of Prior Knowledge
- •14.2 Sparsity in MRI Images
- •14.2.1 Characteristics of MR Images (Prior Knowledge)
- •14.2.2 Choice of Transform
- •14.2.3 Use of Data Ordering
- •14.3 Theory of Compressed Sensing
- •14.3.1 Data Acquisition
- •14.3.2 Signal Recovery
- •14.4 Progress in Sparse Sampling for MRI
- •14.4.1 Review of Results from the Literature
- •14.4.2 Results from Our Work
- •14.4.2.1 PECS
- •14.4.2.2 SENSECS
- •14.4.2.3 PECS Applied to CE-MRA
- •14.5 Prospects for Future Developments
- •References
- •15.1 Introduction
- •15.2 Acquisition of DT Images
- •15.2.1 Fundamentals of DTI
- •15.2.2 The Pulsed Field Gradient Spin Echo (PFGSE) Method
- •15.2.3 Diffusion Imaging Sequences
- •15.2.4 Example: Anisotropic Diffusion of Water in the Eye Lens
- •15.2.5 Data Acquisition
- •15.3 Digital Processing of DT Images
- •15.3.2 Diagonalization of the DT
- •15.3.3 Gradient Calibration Factors
- •15.3.4 Sorting Bias
- •15.3.5 Fractional Anisotropy
- •15.3.6 Other Anisotropy Metrics
- •15.4 Applications of DTI to Articular Cartilage
- •15.4.1 Bovine AC
- •15.4.2 Human AC
- •References
- •Index
Contents
1 |
Introduction .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . |
1 |
|
Geoff Dougherty |
|
2 |
Rapid Prototyping of Image Analysis Applications.. . . . . . . . . . . . . . . . . . . . |
5 |
|
Cris L. Luengo Hendriks, Patrik Malm, and Ewert Bengtsson |
|
3 |
Seeded Segmentation Methods for Medical Image Analysis .. . . . . . . . . . |
27 |
|
Camille Couprie, Laurent Najman, and Hugues Talbot |
|
4 |
Deformable Models and Level Sets in Image Segmentation . . . . . . . . . . . |
59 |
|
Agung AlÞansyah |
|
5 |
Fat Segmentation in Magnetic Resonance Images . .. . . . . . . . . . . . . . . . . . . . |
89 |
|
David P. Costello and Patrick A. Kenny |
|
6 |
Angiographic Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . |
115 |
|
Olena Tankyevych, Hugues Talbot, Nicolas Passat, Mariano |
|
|
Musacchio, and Michel Lagneau |
|
7 |
Detecting and Analyzing Linear Structures in Biomedical |
|
|
Images: A Case Study Using Corneal Nerve Fibers . . . . . . . . . . . . . . . . . . . . |
145 |
|
Mohammad A. Dabbah, James Graham, Rayaz A. Malik, |
|
|
and Nathan Efron |
|
8 |
High-Throughput Detection of Linear Features: Selected |
|
|
Applications in Biological Imaging . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . |
167 |
|
Luke Domanski, Changming Sun, Ryan Lagerstrom, Dadong |
|
|
Wang, Leanne Bischof, Matthew Payne, and Pascal Vallotton |
|
9 |
Medical Imaging in the Diagnosis of Osteoporosis and |
|
|
Estimation of the Individual Bone Fracture Risk . . .. . . . . . . . . . . . . . . . . . . . |
193 |
|
Mark A. Haidekker and Geoff Dougherty |
|
xi
xii |
|
Contents |
10 |
Applications of Medical Image Processing in the Diagnosis |
|
|
and Treatment of Spinal Deformity . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . |
. . . 227 |
|
Clayton Adam and Geoff Dougherty |
|
11 |
Image Analysis of Retinal Images . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . |
. . . 249 |
|
Michael J. Cree and Herbert F. Jelinek |
|
12 |
Tortuosity as an Indicator of the Severity of Diabetic Retinopathy |
.. . 269 |
|
Michael Iorga and Geoff Dougherty |
|
13 |
Medical Image Volumetric Visualization: Algorithms, |
|
|
Pipelines, and Surgical Applications . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . |
. . 291 |
|
Qi Zhang, Terry M. Peters, and Roy Eagleson |
|
14 |
Sparse Sampling in MRI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . |
. 319 |
|
Philip J. Bones and Bing Wu |
|
15 |
Digital Processing of Diffusion-Tensor Images |
|
|
of Avascular Tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . |
. 341 |
|
Konstantin I. Momot, James M. Pope, and R. Mark Wellard |
|
Index . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . |
. 373 |
Contributors
Clayton Adam Queensland University of Technology, Brisbane, Australia, c.adam@qut.edu.au
Agung Alfiansyah Surya Research and Education Center, Tangerang, Indonesia, agung.alÞansyah@gmail.com
Ewert Bengtsson Swedish University of Agricultural Sciences, Uppsala, Sweden Uppsala University, Uppsala, Sweden, ewart.bengtsson@cb.uu.se
Leanne Bischof CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, leanne.bischof@csiro.au
Philip J. Bones University of Canterbury, Christchurch, New Zealand, phil.bones@canterbury.ac.nz
David P. Costello Mater Misericordiae University Hospital and University Collage Dublin, Ireland, dcostello@mater.ie
Camille Couprie Universit« Paris-Est, Paris, France, c.couprie@esiee.fr
Michael J. Cree University of Waikato, Hamilton, New Zealand, cree@waikato.ac.nz
Mohammad A. Dabbah The University of Manchester, Manchester, England, m.a.dabbah@manchester.ac.uk
Luke Domanski CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, Luke.Domanski@csiro.au
Geoff Dougherty California State University Channel Islands, Camarillo, CA, USA, geoff.dougherty@csuci.edu
Roy Eagleson The University of Western Ontario, London, ON, Canada, eagleson@uwo.ca
xiii
xiv |
Contributors |
Nathan Efron Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia, n.efron@qut.edu.au
James Graham The University of Manchester, Manchester, England, jim.graham@manchester.ac.uk
Mark A. Haidekker University of Georgia, Athens, Georgia, mhaidekker.uga@gmail.com
Cris L. Luengo Hendriks Uppsala University, Uppsala, Sweden, cris@cb.uu.se
Michael Iorga NPHS, Thousand Oaks, CA, USA, michael.iorga@yahoo.com
Herbert F. Jelinek Charles Stuart University, Albury, Australia, hjelinek@csu.edu.au
Patrick A. Kenny Mater Misericordiae University Hospital and University College Dublin, Ireland, pkenny@mater.ie
Ryan Lagerstrom CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, Ryan.Lagerstrom@csiro.au
Michel Lagneau Höopital Louis-Pasteur, Colmar, France, michel.lagneau@ch-colmar.rss.fr
Rayaz A. Malik The University of Manchester, Manchester, England,
Rayaz.A.Malik@manchester.ac.uk
Patrik Malm Swedish University of Agricultural Sciences, Uppsala, Sweden
Uppsala University, Uppsala, Sweden, patrik@cb.uu.se
Konstantin I. Momot Queensland University of Technology, Brisbane, Australia, k.momot@qut.edu.au
Mariano Musacchio Höopital Louis-Pasteur, Colmar, France, mariano musacchio@yahoo.fr
Laurent Najman Universit« Paris-Est, Paris, France, l.najman@esiee.fr
Nicholas Passat Universit« de Strasbourg, Strasbourg, France, passat@dpt-info.u-strasbg.fr
Matthew Payne CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, matthew.payne@csiro.au
Terry M. Peters Robarts Research Institute, University of Western Ontario, London, ON, Canada, tpeters@robarts.ca
Contributors |
xv |
James M. Pope Queensland University of Technology, Brisbane, Australia, j.pope@qut.edu.au
Changming Sun CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, changmin.sun@csiro.au
Hugues Talbot Universit« Paris-Est, Paris, France, h.talbot@esiee.fr
Olena Tankyevych Universit« Paris-Est, Paris, France, tankyevych@gmail.com
Pascal Vallotton CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, Pascal.Vallotton@csiro.au
Dadong Wang CSIRO (Commonwealth ScientiÞc and Industrial Research Organisation), North Ryde, Australia, dadong.wang@csiro.au
R. Mark Wellard Queensland University of Technology, Brisbane, Australia, m.wellard@qut.edu.au
Bing Wu Duke University, Durham, NC, USA, contactbing@gmail.com
Qi Zhang Robarts Research Institute, University of Western Ontario, London, ON, Canada, Qi.Zhang@nrc-cnrc.gc.ca