Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology (Advances in Computer Vision and Pattern Recognition)

ISBN 10: 1447110439 / ISBN 13: 9781447110439

Editore: Springer, 2012

Riguardo questo articolo

LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS.

Codice articolo ERICA77314471104396

Dati bibliografici

• Titolo: Advanced Algorithmic Approaches to Medical Image Segmentation: State-of-the-Art Applications in Cardiology, Neurology, Mammography and Pathology (Advances in Computer Vision and Pattern Recognition)
• Editore: Springer
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 1447110439
• ISBN 13: 9781447110439
• Rilegatura: Paperback
• Condizione: Like New
• Tipo di libro: book

Descrizione articolo

Riassunto

Medical imaging is an important topic which is generally recognised as key to better 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 state-of-the-art model-based segmentation techniques which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors based in both industry and academia and presents a host of new material including algorithms for:
- brain segmentation applied to MR;
- neuro-application using MR;
- parametric and geometric deformable models for brain segmentation;
- left ventricle segmentation and analysis using least squares and constrained least squares models for cardiac X-rays;
- left ventricle analysis in echocardioangiograms;
- breast lesion detection in digital mammograms;
detection of cells in cell images.
As an overview of the latest techniques, this book will be of particular interest to students and researchers in medical engineering, image processing, computer graphics, mathematical modelling and data analysis. It will also be of interest to researchers in the fields of mammography, cardiology, pathology and neurology.

Contenuti

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 Note on fMRI Time Course Data Analysis.- 4.7.5 Measure of Cortex Geometry.- 4.8 Discussions: Advantages, Validation and New Challenges i 2-D.- 4.8.1 Advantages of Regional Geometric Boundary/Surfaces.- 4.8.2 Validation of 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.3 Challenges in 2-D and 3-D Cortical Segmentation Algorithms.- 4.8.4 Challenges in fMRI.- 4.9 Conclusions and the Future.- 4.9.1 Acknowledgements.- 5. Segmentation for Multiple Sclerosis Lesion.- 5.1 Introduction.- 5.2 Segmentation Techniques.- 5.2.1 Multi-Spectral Techniques.- 5.2.2 Feature Space Classification.- 5.2.3 Supervised Segmentation.- 5.2.4 Unsupervised Segmentation.- 5.2.5 Automatic Segmentation.- 5.3 AFFIRMATIVE Images.- 5.4 Image Pre-Processing.- 5.4.1 RF Inhomogeneity Correction.- 5.4.2 Image Stripping.- 5.4.3 Three Dimensional MR Image Registration.- 5.4.4 Segmentation.- 5.4.5 Flow Correction.- 5.4.6 Evaluation and Validation.- 5.5 Quantification of Enhancing Multiple Sclerosis Lesions.- 5.6 Quadruple Contrast Imaging.- 5.7 Discussion.- 5.7.1 Acknowledgements.- 6. Finite Mixture Models.- 6.1 Introduction.- 6.2 Pixel Labeling Using the Classical Mixture Model.- 6.3 Pixel Labeling Using the Spatially Variant Mixture Model.- 6.4 Comparison of CMM and SVMM for Pixel Labeling.- 6.5 Bayesian Pixel Labeling Using the SVMM.- 6.6 Segmentation Results.- 6.6.1 Computer Simulations.- 6.6.2 Application to Magnetic Resonance Images.- 6.7 Practical Aspects.- 6.8 Summary.- 6.9 Acknowledgements.- 7. MR Spectroscopy.- 7.1 Introduction.- 7.2 A Short History of Neurospectroscopic Imaging and Segmentation in Alzheimer’s Disease and Multiple Sclerosis.- 7.2.1 Alzheimer’s Disease.- 7.2.2 Multiple Sclerosis.- 7.3 Data Acquisition and Image Segmentation.- 7.3.1 Image Pre-Processing for Segmentation.- 7.3.2 Image Post-Processing for Segmentation.- 7.4 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation in Multiple Sclerosis.- 7.4.1 Automatic MRSI Segmentation and Image Processing Algorithm.- 7.4.2 Relative Metabolite Concentrations and Contribution of Gray Matter and White Matter in the Normal Human Brain.- 7.4.3 MRSI and Gadolinium-Enhanced (Gd).- 7.4.4 Lesion Load and Metabolite Concentrations by Segmentation and MRSI.- 7.4.5 MR Spectroscopic Imaging and Localization for Segmentation.- 7.4.6 Lesion Segmentation and Quantification.- 7.4.7 Magnetic Resonance Spectroscopic Imaging and Segmentation Data Processing.- 7.4.8 Statistical Analysis.- 7.5 Proton Magnetic Resonance Spectroscopic Imaging and Segmentation of Alzheimer’s Disease.- 7.5.1 MRSI Data Acquisition Methods.- 7.5.2 H-1 MR Spectra Analysis.- 7.6 Applications of Magnetic Resonance Spectroscopic Imaging and Segmentation.- 7.6.1 Multiple Sclerosis Lesion Metabolite Characteristics and Serial Changes.- 7.6.2 zheimer’s Disease Plaque Metabolite Characteristics.- 7.7 Discussion.- 7.8 Conclusion.- 7.8.1 Acknowledgements.- 8. Fast WM/GM Boundary Estimation.- 8.1 Introduction.- 8.2 Derivation of the Regional Geometric Active Contour Model from the Classical Parametric Deformable Model.- 8.3 Numerical Implementation of the Three Speed Functions in the Level Set Framework for Geometric Snake Propagation.- 8.3.1 Regional Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.2 Gradient Speed Term Expressed in Terms of the Level Set Function (ø).- 8.3.3 Curvature Speed Term Expressed in Terms of the Level Set Function (ø).- 8.4 Fast Brain Segmentation System Based on Regional Level Sets.- 8.4.1 Overall System and Its Components.- 8.4.2 Fuzzy Membership Computation/Pixel Classification.- 8.4.3 Eikonal Equation and its Mathematical Solution.- 8.4.4 Fast Marching Method for Solving the Eikonal Equation.- 8.4.5 A Note on the Heap Sorting Algorithm.- 8.4.6 Segmentation Engine: Running the Level Set Method in the Narrow Band.- 8.5 MR Segmentation Results on Synthetic and Real Data.- 8.5.1 Input Data Set and Input Level Set Parameters.- 8.5.2 Results: Synthetic and Real.- 8.5.3 Numerical Stability, Signed Distance Transformation Computation, Sensitivity of Parameters and Speed Issues.- 8.6 Advantages of the Regional Level Set Technique.- 8.7 Discussions: Comparison with Previous Techniques.- 8.8 Conclusions and Further Directions.- 8.8.1 Acknowledgements.- 9. Digital Mammography Segmentation.- 9.1 Introduction.- 9.2 Image Segmentation in Mammography.- 9.3 Anatomy of the Breast.- 9.4 Image Acquisition and Formats.- 9.4.1 Digitization of X-Ray Mammograms.- 9.4.2 Image Formats.- 9.4.3 Image Quantization and Tree-Pyramids.- 9.5 Mammogram Enhancement Methods.- 9.6 Quantifying Mammogram Enhancement.- 9.7 Segmentation of Breast Profile.- 9.8 Segmentation of Microcalcifications.- 9.9 Segmentation of Masses.- 9.9.1 Global Methods.- 9.9.2 Edge-Based Methods.- 9.9.3 Region-Based Segmentation.- 9.9.4 ROI Detection Techniques Using a Single Breast.- 9.9.5 ROI Detection Techniques Using Breast Symmetry.- 9.9.6 Detection of Spicules.- 9.9.7 Breast Alignment for Segmentation.- 9.10 Measures of Segmentation and Abnormality Detection.- 9.11 Feature Extraction From Segmented Regions.- 9.11.1 Morphological Features.- 9.11.2 Texture Features.- 9.11.3 Other Features.- 9.12 Public Domain Databases in Mammography.- 9.12.1 The Digital Database for Screening Mammography (DDSM).- 9.12.2 LLNL/UCSF Database.- 9.12.3 Washington University Digital Mammography Database.- 9.12.4 The Mammographic Image Analysis Society (MIAS) Database.- 9.13 Classification and Measures of Performance.- 9.13.1 Classification Techniques.- 9.13.2 The Receiver Operating Characteristic Curve.- 9.14 Conclusions.- 9.15 Acknowledgements.- 10. Cell Image Segmentation for Diagnostic Pathology.- 10.1 Introduction.- 10.2 Segmentation.- 10.2.1 Feature Space Analysis.- 10.2.2 Mean Shift Procedure.- 10.2.3 Cell Segmentation.- 10.2.4 Segmentation Examples.- 10.3 Decision Support System for Pathology.- 10.3.1 Problem Domain.- 10.3.2 System Overview.- 10.3.3 Current Database.- 10.3.4 Analysis of Visual Attributes.- 10.3.5 Overall Dissimilarity Metric.- 10.3.6 Performance Evaluation and Comparisons.- 10.4 Conclusion.- 11. The Future in Segmentation.- 11.1 Future Research in Medical Image Segmentation.- 11.1.1 The Future of MR Image Generation and Physical Principles.- 11.1.2 The Future of Cardiac Imaging.- 11.2.3 The Future of Neurological Segmentation.- 11.2.4 The Future in Digital Mammography.- 11.2.5 The Future of Pathology Image Segmentation.