- •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
15 Digital Processing of Diffusion-Tensor Images of Avascular Tissues |
345 |
15.2 Acquisition of DT Images
15.2.1 Fundamentals of DTI
DT images can be obtained using Nuclear Magnetic Resonance (NMR). NMR measures the frequency of precession of nuclear spins such as that of the proton (1H), which in a magnetic field B0, is given by the Larmor equation:
ω0 = γ B0. |
(15.7) |
The key to achieving spatial resolution in MRI is the application of time-dependent magnetic field gradients that are superimposed on the (ideally uniform) static magnetic field B0. In practice, the gradients are applied via a set of dedicated 3-axis gradient coils, each of which is capable of applying a gradient in one of the orthogonal directions (x, y, and z). Thus, in the presence of a magnetic field gradient g,
g = |
∂ Bz |
, |
∂ Bz |
, |
∂ Bz |
(15.8) |
|
∂ x |
|
∂ y |
|
∂ z |
|
the magnetic field strength, and hence the precession frequency become position dependent. The strength of the magnetic field experienced by a spin at position r is given by:
B = B0 + g ·r |
(15.9) |
The corresponding Larmor precession frequency is changed by the contribution from the gradient:
∂ φ(r)
ω(r) = ∂ = γ (B0 + g ·r). (15.10) t
The precession frequency ω is the rate of change of the phase, φ, of a spin – that is, its precession angle in the transverse plane (Fig. 15.3). Therefore, the timedependent phase φ is the integral of the precession frequency over time. In MRI, we switch gradients on and off in different directions to provide spatial resolution, so the gradients are time dependent and the phase of a spin is given by:
t |
t |
|
φ(r, t) = |
ω(r, t )dt = γB0t + γ g(t ) ·rdt . |
(15.11) |
0 |
0 |
|
We observe the phase relative to the reference frequency given by (15.7). For example if the gradient is applied in the x direction in the form of a rectangular pulse of amplitude gx and duration δ the additional phase produced by the gradients is
346 |
K.I. Momot et al. |
Fig. 15.3 The effect of a magnetic field gradient on precession of spins. A constant magnetic field gradient g (illustrated by the blue ramp) applied in some arbitrary direction changes the magnetic field at position r from B0 to a new value B = B0 + g ·r. The gradient perturbs the precession of the spins, giving rise to an additional position-dependent phase φ , which may be positive or negative depending on whether the magnetic field produced by the gradient coils strengthens or weakens the static magnetic field B0
δ |
|
φ (r, t) = γ gx(t )xdt = γδgxx = 2πkxx, |
(15.12) |
0 |
|
where the “spatial frequency” kx = γδgx/2π is also known as the “k value”. It plays an important role in the description of spatial encoding in MRI and can be thought of as the frequency of spatial harmonic functions used to encode the image.
In MRI to achieve spatial resolution in the plane of the selected slice (x, y), we apply gradients in both x and y directions sequentially. The NMR signal is then sampled for a range of values of the corresponding spatial frequencies kx and ky.
For one of these gradients (gx, say), this is achieved by keeping the amplitude fixed and incrementing the time t at which the signal is recorded (the process called ‘frequency encoding’).
In the case of the orthogonal gradient (gy), the amplitude of the gradient is stepped through an appropriate series of values. For this gradient, the appropriate spatial frequency can be written:
δ |
|
ky = γ gy(t )dt = γ δ gy/2π. |
(15.13) |
0
15 Digital Processing of Diffusion-Tensor Images of Avascular Tissues |
347 |
Fig. 15.4 Gradient pulse pairs used for diffusion attenuation. The first gradient sensitizes the magnetisation of the sample to diffusional displacement by winding a magnetization helix. The second gradient rewinds the helix and thus enables the measurement of the diffusion-attenuated signal. The two gradients must have the same amplitude if they are accompanied by the refocusing RF π pulse; otherwise, their amplitudes must be opposite
The MR image is then generated from the resulting two-dimensional data set S(kx, ky) by Fourier transformation:
S(x, y) = S(kx, ky)e−2πi(kx x+kyy)dkxdky. (15.14)
The Fourier transform relationship between an MR image and the raw NMR data is analogous to that between an object and its diffraction pattern.
15.2.2 The Pulsed Field Gradient Spin Echo (PFGSE) Method
Consider the effect of a gradient pair consisting of two consecutive gradient pulses of opposite sign shown in Fig. 15.4 (or alternatively two pulses of the same sign separated by the 180◦ RF pulse in a ‘spin echo’ sequence).
It is easy to show that spins moving with velocity v acquire a net phase shift (relative to stationary spins) that is independent of their starting location and given by:
φ(v) = −γ g ·vδ , |
(15.15) |
where δ is the duration of each gradient in the pair and |
is the separation of |
the gradients. Random motion of the spins gives rise to a phase dispersion and attenuation of the spin echo NMR signal.
Stejskal and Tanner [3] showed in the 1960s that, for a spin echo sequence this
additional attenuation (Fig. 15.5) takes the form: |
|
S( , g) = S0e−TE/T2 e−Dγ2g2δ2( −δ/3). |
(15.16) |
The first term is the normal echo attenuation due to transverse (spin-spin) relaxation. By stepping out the echo time TE, we can measure T2.
The second term is the diffusion term. By incrementing the amplitude of the magnetic field gradient pulses (g), we can measure the self-diffusion coefficient D.
For a fixed echo time TE, we write:
S = S0e−bD = S0e−TE/T2 e−bD, |
(15.17) |
348 |
K.I. Momot et al. |
Fig. 15.5 A pulsed field gradient spin echo (PGSE) sequence showing the effects of diffusive attenuation on spin echo amplitude
where |
|
|
δ |
|
||
b = γ2g2δ2 |
− |
(15.18) |
||||
|
. |
|||||
3 |
||||||
The ADC is then given by: |
|
|
|
|
|
|
ADC = −ln |
S |
|
b |
(15.19) |
||
|
|
|
||||
S |
|
|||||
|
0 |
|
|
|
|
For the case of anisotropic diffusion described by a diffusion tensor D, the expression for the echo attenuation in a PFG spin echo experiment becomes:
S( , g) = S0e−γ2g·D·gδ2( −δ/3), |
(15.20) |
where g = (gx, gy, gz) is the gradient vector, and the scalar product g ·D ·g is defined analogously to (15.5).
Overall, if diffusion is anisotropic, the echo attenuation will have an orientation dependence with respect to the measuring gradient g. Gradients along the x, y, and z directions sample, respectively, the diagonal elements Dxx, Dyy, and Dzz of the DT. In order to sample the off-diagonal elements, we must apply gradients in oblique directions – that is combinations of gx and gy or gy and gz, etc. Because the DT is symmetric, there are just 6 independent elements. To fully determine the DT therefore requires a minimum of 7 separate measurements – for example:
gx |
0 |
|
g |
0 |
0 |
1 |
g |
1 |
g |
1 |
0 |
|||
gy |
= 0 |
|
0 |
|
0 |
|
0 |
|
||||||
, |
, g , |
, √ |
|
g , √ |
|
, √ |
|
g . |
||||||
2 |
2 |
2 |
||||||||||||
gz |
0 |
|
0 |
0 |
g |
|
|
0 |
|
|
g |
|
|
g |
(15.21)