| Description |
1 online resource (489 pages) : illustrations |
| Series |
Electrical engineering and applied signal processing series |
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Electrical engineering and applied signal processing series
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| Contents |
Front cover -- Preface -- The Editor -- Contributors -- Contents -- 1 -- Computer-Aided Diagnosis of Breast Cancer -- 1.1 Introduction -- 1.2 Computerized Detection of Microcalcifications -- 1.2.1 Methods -- 1.2.1.1 Preprocessing Technique -- 1.2.1.2 Microcalcification Segmentation -- 1.2.1.3 Rule-Based False-Positive Reduction -- 1.2.1.4 False-Positive Reduction Using Convolution Neural Network Classifier -- 1.2.1.5 False-Positive Reduction Using Clustering -- 1.2.2 FROC Analysis of Detection Accuracy -- 1.2.3 Effects of Computer-Aided Detection on Radiologists' Performance -- 1.3 Computerized Detection of Masses -- 1.3.1 Methods -- 1.3.1.1 Preprocessing and Segmentation -- 1.3.1.2 Object Refinement -- 1.3.1.3 Feature Extraction and Classification -- 1.3.2 FROC Analysis of Detection Accuracy -- 1.3.2.1 Data Sets -- 1.3.2.2 True Positive and False Positive -- 1.3.2.3 Training and Testing -- 1.3.2.4 Performance of Mass Detection Algorithm -- 1.4 Mass Detection with Two-View Information -- 1.4.1 Methods -- 1.4.1.1 Geometrical Modeling -- 1.4.1.2 One-View Analysis -- 1.4.1.3 Two-View Analysis -- 1.4.1.4 Fusion Analysis -- 1.4.2 Results -- 1.4.2.1 Geometrical Modeling -- 1.4.2.2 Comparison of One-View and Two-View Analysis -- 1.5 Summary -- Acknowledgment -- References -- 2 -- Medical-Image Processing and Analysis for CAD Systems -- 2.1 Introduction -- 2.2 Basics of a CAD System -- 2.2.1 Computer-Aided Methodologies in Mammography -- 2.2.2 Historical Overview -- 2.2.3 CAD Architecture -- 2.2.4 Preprocessing -- 2.2.5 Segmentation -- 2.2.6 Feature Analysis (Extraction, Selection, and Validation) -- 2.2.7 Classification System (Reduction of False Positives or Characterization of Lesions) -- 2.2.7.1 Conventional Classifiers -- 2.2.7.2 Artificial Neural Networks (ANNs) -- 2.2.7.3 Fuzzy-Logic Systems -- 2.2.7.4 Support-Vector Machines |
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2.2.8 Evaluation Methodologies -- 2.2.9 Integrated CAD Systems -- 2.3 Computer-Aided Methodologies for Three- Dimensional Reconstruction of an Artery -- 2.3.1 IVUS Image Interpretation -- 2.3.2 Automated Methods for IVUS ROI Detection -- 2.3.2.1 IVUS Image Preprocessing -- 2.3.2.2 IVUS Image Segmentation -- 2.3.3 Limitations in Quantitative IVUS Image Analysis -- 2.3.4 Plaque Characterization in IVUS Images -- 2.3.5 Three-Dimensional Reconstruction -- 2.4 Conclusions -- References -- 3 -- Texture and Morphological Analysis of Ultrasound Images of the Carotid Plaque for the Assessment of Stroke -- 3.1 Introduction -- 3.1.1 Ultrasound Vascular Imaging -- 3.1.2 Previous Work on the Characterization of Carotid Plaque -- 3.2 Materials -- 3.3 The Carotid Plaque Multifeature, Multiclassifier System -- 3.3.1 Image Acquisition and Standardization -- 3.3.2 Plaque Identification and Segmentation -- 3.3.3 Feature Extraction -- 3.3.3.1 Statistical Features (SF) -- 3.3.3.2 Spatial Gray-Level-Dependence Matrices (SGLDM) -- 3.3.3.3 Gray-Level Difference Statistics (GLDS) -- 3.3.3.4 Neighborhood Gray-Tone-Difference Matrix (NGTDM) -- 3.3.3.5 Statistical-Feature Matrix (SFM) -- 3.3.3.6 Laws's Texture Energy Measures (TEM) -- 3.3.3.7 Fractal Dimension Texture Analysis (FDTA) -- 3.3.3.8 Fourier Power Spectrum (FPS) -- 3.3.3.9 Shape Parameters -- 3.3.3.10 Morphological Features -- 3.3.4 Feature Selection -- 3.3.5 Plaque Classification -- 3.3.5.1 Classification with the SOM Classifier -- 3.3.5.2 Classification with the KNN Classifier -- 3.3.6 Classifier Combiner -- 3.3.6.1 Majority Voting -- 3.3.6.2 Weighted Averaging Based on a Confidence Measure -- 3.4 Results -- 3.4.1 Feature Extraction and Selection -- 3.4.2 Classification Results of the SOM Classifiers -- 3.4.3 Classification Results of the KNN Classifiers -- 3.4.4 Results of the Classifier Combiner |
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3.4.5 The Proposed System -- 3.4.5.1 Training of the System -- 3.4.5.2 Classification of a New Plaque -- 3.5 Discussion -- 3.5.1 Feature Extraction and Selection -- 3.5.2 Plaque Classification -- 3.5.3 Classifier Combiner -- 3.6 Conclusions and Future Work -- Appendix 3.1 Texture-Feature-Extraction Algorithms -- A3.1.1 Statistical Features -- A3.1.1.1 Mean Value -- A3.1.1.2 Median Value -- A3.1.1.3 Standard Deviation -- A3.1.1.4 Skewness -- A3.1.1.5 Kurtosis -- A3.1.2 Spatial Gray-Level-Dependence Matrices (SGLDM) -- A3.1.2.1 Notation -- A3.1.2.2 Texture Measures -- A3.1.2.3 Extracted SGLDM Features -- A3.1.3 Gray-Level-Difference Statistics (GLDS) -- A3.1.3.1 Contrast -- A3.1.3.2 Angular Second Moment -- A3.1.3.3 Entropy -- A3.1.3.4 Mean -- A3.1.4 Neighborhood Gray-Tone-Difference Matrix (NGTDM) -- A3.1.4.1 Coarseness -- A3.1.4.2 Contrast -- A3.1.4.3 Busyness -- A3.1.4.4 Complexity -- A3.1.4.5 Strength -- A3.1.5 Statistical-Feature Matrix (SFM) -- A3.1.5.1 Coarseness -- A3.1.5.2 Contrast -- A3.1.5.3 Periodicity -- A3.1.5.4 Roughness -- A3.1.6 Laws's Texture Energy Measures (TEM) -- A3.1.7 Fractal Dimension Texture Analysis (FDTA) -- A3.1.8 Fourier Power Spectrum (FPS) -- A3.1.8.1 Radial Sum -- A3.1.8.2 Angular Sum -- A3.1.9 Shape Parameters -- A3.1.10 Morphological Features -- Acknowledgment -- References -- 4 -- Biomedical-Image Classification Methods and Techniques -- 4.1 Introduction -- 4.2 General Image Classification -- 4.2.1 Unsupervised Clustering Algorithms -- 4.2.1.1 k-Means Clustering -- 4.2.1.2 ISODATA -- 4.2.1.3 Fuzzy c-Means -- 4.2.2 Basic Supervised Classifiers -- 4.2.2.1 Minimum-Distance Classifier -- 4.2.2.2 Bayesian Classifier -- 4.2.2.3 k-Nearest Neighbor -- 4.2.2.4 Decision Tree -- 4.2.3 Neural Networks -- 4.2.3.1 Supervised Neural Networks -- 4.2.3.2 Unsupervised Neural Networks -- 4.2.4 Contextual Classifier |
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4.3 Modern Advances in Classification of Biomedical Images -- 4.3.1 Kernel-Based Methods -- 4.3.1.1 Support Vector Machines -- 4.3.1.2 Kernel Principal-Component Analysis -- 4.3.2 Independent Component Analysis -- 4.3.3 Ensembles of Classifiers -- 4.3.4 Distributed Methods -- 4.3.4.1 Particle Filters -- 4.3.4.2 Model-Based Search Methods -- 4.4 Conclusion -- References -- 5 -- Texture Characterization Using Autoregressive Models with Application to Medical Imaging -- 5.1 Introduction -- 5.1.1 One-Dimensional Autoregressive Modeling for Biomedical Signals -- 5.1.2 Two-Dimensional Autoregressive Modeling for Biomedical Signals -- 5.2 Two-Dimensional Autoregressive Model -- 5.3 Yule-Walker System of Equations -- 5.4 Extended Yule-Walker System of Equations in the Third-Order Statistical Domain -- 5.5 Constrained-Optimization Formulation with Equality Constraints -- 5.5.1 Simulation Results -- 5.6 Constrained Optimization with Inequality Constraints -- 5.6.1 Constrained-Optimization Formulation with Inequality Constraints 1 -- 5.6.2 Constrained-Optimization Formulation with Inequality Constraints 2 -- 5.6.3 Simulation Results -- 5.7 AR Modeling with the Application of Clustering Techniques -- 5.7.1 Hierarchical Clustering Scheme for AR Modeling -- 5.7.2 k-Means Algorithm for AR Modeling -- 5.7.3 Selection Scheme -- 5.7.4 Simulation Results -- 5.8 Applying AR Modeling to Mammography -- 5.8.1 Mammograms with a Malignant Mass -- 5.8.1.1 Case 1: mdb023 -- 5.8.1.2 Case 2: mdb028 -- 5.8.1.3 Case 3: mdb058 -- 5.8.2 Mammograms with a Benign Mass -- 5.8.2.1 Case 1: mdb069 -- 5.8.2.2 Case 2: mdb091 -- 5.8.2.3 Case 3: mdb142 -- 5.9 Summary and Conclusion -- References -- 6 -- Locally Adaptive Wavelet Contrast Enhancement -- 6.1 Introduction -- 6.2 Background -- 6.3 Materials and Methods -- 6.3.1 Discrete Dyadic Wavelet Transform Review |
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6.3.2 Redundant Dyadic Wavelet Transform -- 6.3.3 Wavelet Denoising -- 6.3.3.1 Noise Suppression by Wavelet Shrinkage -- 6.3.3.2 Adaptive Wavelet Shrinkage -- 6.3.4 Wavelet Contrast Enhancement -- 6.3.4.1 Global Wavelet Mapping -- 6.3.4.2 Adaptive Wavelet Mapping -- 6.3.5 Implementation -- 6.3.6 Test Image Demonstration and Quantitative Evaluation -- 6.4 Observer Performance Evaluation -- 6.4.1 Case Sample -- 6.4.2 Observer Performance -- 6.4.3 Statistical Analysis -- 6.4.3.1 Wilcoxon Signed Ranks Test -- 6.4.3.2 ROC Analysis -- 6.4.4 Results -- 6.4.4.1 Detection Task -- 6.4.4.2 Morphology Characterization Task -- 6.4.4.3 Pathology Classification Task -- 6.5 Discussion -- Acknowledgment -- References -- 7 -- Three-Dimensional Multiscale Watershed Segmentation of MR Images -- 7.1 Introduction -- 7.2 Watershed Analysis -- 7.2.1 The Watershed Transformation -- 7.2.1.1 The Continuous Case -- 7.2.1.2 The Discrete Case -- 7.2.1.3 The 3-D Case -- 7.2.1.4 Algorithms about Watersheds -- 7.2.2 The Gradient Watersheds -- 7.2.3 Oversegmentation: A Pitfall to Solve in Watershed Analysis -- 7.3 Scale-Space and Segmentation -- 7.3.1 The Notion of Scale -- 7.3.2 Linear (Gaussian) Scale-Space -- 7.3.3 Scale-Space Sampling -- 7.3.4 Multiscale Image-Segmentation Schemes -- 7.3.4.1 Design Issues -- 7.3.4.2 The State of the Art -- 7.4 The Hierarchical Segmentation Scheme -- 7.4.1 Gradient Magnitude Evolution -- 7.4.2 Watershed Lines during Gradient Magnitude Evolution -- 7.4.3 Linking across Scales -- 7.4.4 Gradient Watersheds and Hierarchical Segmentation in Scale-Space -- 7.4.5 The Salient-Measure Module -- 7.4.5.1 Watershed Valuation in the Superficial Structure- Dynamics of Contours -- 7.4.5.2 Dynamics of Gradient Watersheds in Scale-Space -- 7.4.6 The Stopping-Criterion Stage -- 7.4.7 The Intelligent Interactive Tool -- 7.5 Experimental Results |
| Summary |
To successfully detect and diagnose disease, it is vital for medical diagnosticians to properly apply the latest medical imaging technologies. It is a worrisome reality that due to either the nature or volume of some of the images provided, early or obscured signs of disease can go undetected or be misdiagnosed. To combat these inaccuracies, diagnosticians have come to rely on applications that focus on medical image analysis. While there is a vast amount of information available on these procedures, a single-source guide that can comprehensively yet succinctly explain them would be an invaluable resource to have. Medical Image Analysis Methods is that resource. It is an essential reference that details the primary methods, techniques, and approaches used to improve the quality of visually perceived images, as well as, quantitative detection and diagnostic decision aids. The book methodically presents this information by tapping into the expertise of a number of well-known contributing authors and researchers that are at the forefront of medical image analysis. This comprehensive volume illustrates analytical techniques such as, computer-aided diagnosis (CAD), adaptive wavelet image enhancement, and data-driven optimized image segmentation and registration. Paradigms of the analysis methods used in bioinformatics and neurosciences are also provided in respective chapters. In addition, this reference reviews techniques that are used to evaluate these major medical-image processing and analysis methods |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
| Subject |
Diagnostic imaging -- Digital techniques.
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Diagnostic Imaging -- methods
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Image Interpretation, Computer-Assisted -- methods
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Image Processing, Computer-Assisted -- methods
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Diagnostic imaging -- Digital techniques
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| Form |
Electronic book
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| Author |
Costaridou, Lena
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| ISBN |
0849320895 |
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9780849320897 |
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0203500458 |
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9780203500453 |
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0429210361 |
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9780429210365 |
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1135490511 |
|
9781135490515 |
|
1280614838 |
|
9781280614835 |
|
9786610614837 |
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6610614830 |
|
