Advanced Algorithmic Approaches to Medical Image Segmentation

Medical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.

Contenu

1. Principles of Image Generation.- 1.1 Introduction.- 1.2 Ultrasound Image Generation.- 1.2.1 The Principle of Pulse-Echo Ultrasound Imaging.- 1.2.2 B-Scan Quality and the Ultimate Limits.- 1.2.3 Propagation-Related Artifacts and Resolution Limits.- 1.2.3 Attenuation-Related Artifacts.- 1.3 X-Ray Cardiac Image Generation.- 1.3.1 LV Data Acquisition System Using X-Rays.- 1.3.2 Drawbacks of Cardiac Catheterization.- 1.4 Magnetic Resonance Image Generation.- 1.4.1 Physical Principles of Nuclear Magnetic Resonance.- 1.4.2 Basics of Magnetic Resonance Imaging.- 1.4.3 Gradient-Echo (GRE).- 1.4.4 The Latest Techniques for MR Image Generation.- 1.4.5 3-D Turbo FLASH (MP-RAGE) Technique.- 1.4.6 Non-Rectilinear k-Space Trajectory: Spiral.- 1.4.7 Fat Suppression.- 1.4.8 High Speed MRI: Perfusion-Weighted.- 1.4.9 Time of Flight (TOF) MR Angiography.- 1.4.10 Fast Spectroscopic Imaging.- 1.4.11 Recent MR Imaging Techniques.- 1.5 Computer Tomography Image Generation.- 1.5.1 Fourier Reconstruction Method.- 1.6 Positron-Emission Tomography Image Generation.- 1.6.1 Underlying Principles of.- 1.6.2 Usage of PET in Diagnosis.- 1.6.3 Fourier Slice Theorem.- 1.6.4 The Reconstruction Algorithm in PET.- 1.6.5 Image Reconstruction Using Filtered Back-Projection.- 1.7 Comparison of Imaging Modalities: A Summary.- 1.7.1 Acknowledgements.- 2. Segmentation in Echocardiographic Images.- 2.1 Introduction.- 2.2 Heart Physiology and Anatomy.- 2.2.1 Cardiac Function.- 2.2.2 Standard LV Views in 2-DEs.- 2.2.3 LV Function Assessment Using 2-DEs.- 2.3 Review of LV Boundary Extraction Techniques Applied to Echocardiographic Data.- 2.3.1 Acoustic Quantification Techniques.- 2.3.2 Image-Based Techniques.- 2.3.3 2-DE Image Processing Techniques.- 2.4Automatic Fuzzy Reasoning-Based Left Ventricular Center Point Extraction.- 2.4.1 LVCP Extraction System Overview.- 2.4.2 Stage 1: Pre-Processing.- 2.4.3 Stage 2: LVCP Features Fuzzification.- 2.4.4 Template Matching.- 2.4.5 Experimental Results.- 2.4.6 Conclusion.- 2.5 A New Edge Detection in the Wavelet Transform Domain.- 2.5.1 Multiscale Edge Detection and the Wavelet Transform.- 2.5.2 Edge Detection Based on the Global Maximum of Wavelet Transform (GMWT).- 2.5.3 GMWT Performance Analysis and Comparison.- 2.6 LV Segmentation System.- 2.6.1 Overall Reference.- 2.6.2 3D Non-Uniform Radial Intensity Sampling.- 2.6.3 LV Boundary Edge Detection on 3D Radial Intensity Matrix.- 2.6.4 Post-Processing of the Edges and Closed LVE Approximation.- 2.6.5 Automatic LV Volume Assessment.- 2.7 Conclusions.- 2.8 Acknowledgments.- 3. Cardiac Boundary Segmentation.- 3.1 Introduction.- 3.2 Cardiac Anatomy and Data Acquisitions for MR, CT, Ul-trasound and X-Rays.- 3.2.1 Cardiac Anatomy.- 3.2.2 Cardiac MR, CT, Ultrasound and X-Ray Acquisitions.- 3.3 Low- and Medium-Level LV Segmentation Techniques.- 3.3.1 Smoothing Image Data.- 3.3.2 Manual and Semi-Automatic LV Thresholding.- 3.3.3 LV Dynamic Thresholding.- 3.3.4 Edge-Based Techniques.- 3.3.5 Mathematical Morphology-Based Techniques.- 3.3.6 Drawbacks of Low-Level LV Segmentation Techniques.- 3.4 Model-Based Pattern Recognition Methods for LV Modeling.- 3.4.1 LV Active Contour Models in the Spatial and Temporal Domains.- 3.4.2 Model-Based Pattern Recognition Learning Methods.- 3.4.3 Polyline Distance Measure and Performance Terms.- 3.4.4 Data Analysis Using IdCM, InCM and the Greedy Method.- 3.5 Left Ventricle Apex Modeling: A Model-Based Approach.- 3.5.1 Longitudinal Axis and Apex Modeling.- 3.5.2 Ruled Surface Model.- 3.5.3 Ruled Surface sr and its Coefficients.- 3.5.4 Estimation of Robust Coefficients and Coordinates of the Ruled Surface.- 3.5.5 Experiment Design.- 3.5.6 Analytical Error Measure, AQin for Inlier Data.- 3.5.7 Experiments, Results and Discussions.- 3.5.8 Conclusions on LV Apex Modeling.- 3.6 Integration of Low-Level Features in LV Model-Based Cardiac Imaging: Fusion of Two Computer Vision Systems.- 3.7 General Purpose LV Validation Technique.- 3.8 LV Convex Hulling: Quadratic Training-Based Point Modeling.- 3.8.1 Quadratic Vs. Linear Optimization for Convex Hulling.- 3.9 LV Eigen Shape Modeling.- 3.9.1 Procrustes Superposition.- 3.9.2 Dimensionality Reduction Using Constraints for Joint.- 3.10 LV Neural Network Models.- 3.11 Comparative Study and Summary of the Characteristics of Model-Based Techniques.- 3.11.1 Characteristics of Model-Based LV Imaging.- 3.12 LV Quantification: Wall Motion and Tracking.- 3.12.1 LV Wall Motion Measurements.- 3.12.2 LV Volume Measurements.- 3.12.3 LV Wall Motion Tracking.- 3.13 Conclusions.- 3.13.1 Cardiac Hardware.- 3.13.2 Cardiac Software.- 3.13.3 Summary.- 3.13.4 Acknowledgments.- 4. Brain Segmentation Techniques.- 4.1 Introduction.- 4.1.1 Human Brain Anatomy and the MRI System.- 4.1.2 Applications of Brain Segmentation.- 4.2 Brain Scanning and its Clinical Significance.- 4.3 Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.3.1 Atlas-Based and Threshold-Based Techniques.- 4.3.2 Cortical Segmentation Using Probability-Based Techniques.- 4.3.3 Clustering-Based Cortical Segmentation Techniques.- 4.3.4 Mathematical Morphology-Based Cortical Segmentation Techniques.- 4.3.5 Prior Knowledge-Based Techniques.- 4.3.6 Texture-Based Techniques.- 4.3.7 Neural Network-Based Techniques.- 4.3.8 Regional Hyperstack: Fusion of Edge-Diffusion with Region-Linking.- 4.3.9 Fusion of Probability-Based with Edge Detectors, Connectivity and Region-Growing.- 4.3.10 Summary of Region-Based Techniques: Pros and Cons.- 4.4 Boundary/Surface-Based 2-D and 3-D Cortical Segmentation Techniques: Edge, Reconstruction, Parametric and Geometric Snakes/Surfaces.- 4.4.1 Edge-Based Cortical-Boundary Estimation Techniques.- 4.4.2 3-D Cortical Reconstruction From 2-D Serial Cross-Sections (Bourke/Victoria).- 4.4.3 2-D and 3-D Parametric Deformable Models for Cortical Boundary Estimation: Snakes, Fitting, Constrained, Ribbon, T-Surface, Connectedness.- 4.4.4 2-D and 3-D Geometric Deformable Models.- 4.4.5 A Note on Isosurface Extraction (Lorensen/GE).- 4.4.6 Summary of Boundary/Surface-Based Techniques: Pros and Cons.- 4.5 Fusion of Boundary/Surface with Region-Based 2-D and 3-D Cortical Segmentation Techniques.- 4.5.1 2-D/3-D Regional Parametric Boundary: Fusion of Boundary with Classification (Kapur/MIT).- 4.5.2 Regional Parametric Surfaces: Fusion of Surface with Clustering (Xu/JHU).- 4.5.3 2-D Regional Geometric Boundary: Fusion of Boundary with Clustering for Cortical Boundary Estimation (Suri/Marconi).- 4.5.34 3-D Regional Geometric Surfaces: Fusion of Geometric Surface with Probability-Based Voxel Classification (Zeng/Yale).- 4.5.5 2-D/3-D Regional Geometric Surface: Fusion of Geometric Boundary/Surface with Global Shape Information (Leventon/MIT).- 4.5.6 2-D/3-D Regional Geometric Surface: Fusion of Boundary/Surface with Bayesian-Based Pixel Classification (Barillot/IRISA).- 4.5.7 Similarities/Differences Between Different Cortical Segmentation Techniques.- 4.6 3-D Visualization Using Volume Rendering and Texture Mapping.- 4.6.1 Volume Rendering Algorithm for Brain Segmentation.- 4.6.2 Texture Mapping Algorithm for Segmented Brain Visualization.- 4.7 A Note on fMRI: Algorithmic Approach for Establishing the Relationship Between Cognitive Functions and Brain Cortical Anatomy.- 4.7.1 Superiority of fMRI over PET/SPECT Imaging.- 4.7.2 Applications of fMRI.- 4.7.3 Algorithm for Superimposition of Functional and Anatomical Cortex.- 4.7.4 A Short No…