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Medical Imaging : Artificial Intelligence, Image Recognition, and Machine Learning Techniques.

By: Contributor(s): Material type: TextTextPublisher: Milton : Taylor & Francis Group, 2019Copyright date: ©2020Edition: 1st edDescription: 1 online resource (251 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780429642494
Subject(s): Genre/Form: Additional physical formats: Print version:: Medical ImagingDDC classification:
  • 616.0754
LOC classification:
  • RC78.7.D53 .M435 2020
Online resources:
Contents:
Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- 1: A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs -- 1.1 Introduction -- 1.2 Materials and Methods -- 1.2.1 Data Collection and Preprocessing -- 1.2.2 Proposal 1-Feature Extraction Using Local/Global Feature Descriptors and Classification Using SVM -- 1.2.3 Proposal 2-Feature Extraction and Classification Using a Customized CNN -- 1.2.4 Proposal 3-Feature Extraction Using Pre-Trained CNNs and Classification Using SVM -- 1.2.5 Proposal 4-Constructing Stacked Model Ensembles -- 1.3 Results and Discussion -- 1.4 Conclusion and Future Work -- Acknowledgments -- Conflict of Interest -- References -- 2: The Role of Artificial Intelligence (AI) in Medical Imaging: General Radiologic and Urologic Applications -- 2.1 Introduction to Artificial Intelligence (AI) -- 2.1.1 Terminology -- 2.1.2 Practical Costs -- 2.2 Artificial Intelligence in Medicine -- 2.3 Artificial Intelligence in Radiology -- 2.3.1 Extrinsic Factors to Image Interpretation -- 2.3.2 Intrinsic Factors to Image Quality -- 2.3.2.1 Geometry -- 2.3.2.2 Contrast -- 2.3.2.3 Background -- 2.3.3 Specific Technical Example of AI in Medical Imaging -- 2.4 Urologic Applications -- 2.5 Benefits vs. Disadvantages -- 2.6 Future Considerations -- References -- 3: Early Detection of Epileptic Seizures Based on Scalp EEG Signals -- 3.1 Introduction -- 3.2 Electroencephalogram -- 3.3 EEG Signal Processing -- 3.3.1 EEG Data Preprocessing -- 3.3.2 Feature Extraction -- 3.3.3 SVM Implementation -- 3.3.4 Performance Metrics -- 3.4 Results and Discussion -- 3.4.1 Comparison with Studies Following Similar Performance Metrics -- 3.4.2 Overall Performance -- 3.4.3 Using Epoch-Specific Values Instead of Event-Specific Values -- 3.5 Conclusion -- References.
4: Fractal Analysis in Histology Classification of Non-Small Cell Lung Cancer -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Image Analysis -- 4.2.2 Computation of Fractal Dimension -- 4.2.3 Extraction of Radiomics Features -- 4.2.4 Classification -- 4.2.5 Results -- 4.3 Conclusion -- References -- 5: Multi-Feature-Based Classification of Osteoarthritis in Knee Joint X-Ray Images -- 5.1 Introduction -- 5.2 Causes of OA -- 5.3 Levels of Knee OA -- 5.4 Proposed Work -- 5.5 Literature Survey -- 5.6 The Proposed Methodology -- 5.6.1 Noise Removal and Image Enhancement -- 5.6.2 Curvature-Based Feature Extraction Method -- 5.6.3 Segmentation of Image -- 5.6.4 Boundary Extraction -- 5.6.5 Edge Curvature Computation -- 5.6.6 Classification -- 5.6.7 Results and Discussion -- 5.6.7.1 Results for Abnormal Images -- 5.6.7.2 Results for Normal Images -- 5.6.7.3 Classification Results -- 5.7 Texture Analysis-Based Feature Extraction Method -- 5.7.1 Segmentation -- 5.7.2 Locating the Center of the Synovial Cavity -- 5.7.3 Feature Extraction -- 5.7.4 Classification -- 5.7.5 Results and Discussion -- 5.7.5.1 Results for Abnormal Image -- 5.7.5.2 Comparison -- 5.7.5.3 Failure Analysis -- 5.8 Conclusion -- Acknowledgment -- References -- 6: Detection and Classification of Non-Proliferative Diabetic Retinopathy Lesions -- 6.1 Introduction -- 6.2 Methodology -- 6.2.1 Preprocessing -- 6.2.1.1 RGB Color Separation -- 6.2.1.2 Mask Separation -- 6.2.1.3 Image Enhancement -- 6.2.1.4 Histogram Equalization -- 6.2.2 Removal of Optic Disc from Fundus Images -- 6.3 Detection of Microaneurysms -- 6.4 Detection of Hemorrhages -- 6.5 Detection of EXs -- 6.6 Extraction of Retinal Blood Vessels -- 6.7 Experimental Work -- 6.7.1 Extraction of Mask -- 6.7.2 Removal of OD -- 6.7.3 Detection of MAs -- 6.7.4 Detection of EXs -- 6.7.5 Detection of Hemorrhages.
6.7.6 Statistical Techniques on NPDR Lesions -- 6.7.6.1 Statistical Techniques on MAs -- 6.7.7 Statistical Techniques on EXs -- 6.7.8 Statistical Techniques on Hemorrhages -- 6.7.9 Statistical Techniques on Retinal Blood Vessels -- 6.7.10 Grading NPDR Lesions Using ANN -- 6.7.11 K-Means Clustering -- 6.7.12 Performance Measurement by Receiver Operating Characteristic Curve -- 6.8 Conclusion -- References -- 7: Segmentation and Analysis of CT Images for Bone Fracture Detection and Labeling -- 7.1 Introduction -- 7.2 Clinical Aspects -- 7.2.1 Anatomy of Long Bone -- 7.2.2 CT Imaging -- 7.2.3 Types of Fractures -- 7.3 Literature Survey -- 7.4 Proposed Methodology -- 7.4.1 Data Acquisition -- 7.4.2 Data Annotation -- 7.4.3 Unwanted Artifacts Removal -- 7.4.3.1 Histogram Stretching -- 7.4.4 Bone Region Extraction and Labeling -- 7.4.4.1 Seed Point Selection and Spreading -- 7.4.4.2 Threshold Value Definition -- 7.4.4.3 Unique Label Assignment -- 7.5 Results -- 7.5.1 Application to Real Patient-Specific Images -- 7.5.2 Clinical Ground Truth -- 7.5.3 Comparison with State-of-the-Art Methods -- 7.6 Conclusions -- Acknowledgment -- References -- 8: A Systematic Review of 3D Imaging in Biomedical Applications -- 8.1 Introduction -- 8.2 Volumetric Data -- 8.2.1 Data Acquisition -- 8.2.2 Volume Data -- 8.2.3 Grid Structures -- 8.2.4 Volume Visualization -- 8.2.5 Steps in Volume Visualization -- 8.2.5.1 Data Acquisition and Dimension Reconstruction -- 8.2.5.2 Data Preprocessing and Extraction -- 8.2.5.3 View Definition -- 8.3 Indirect Volume Rendering (Surface Fitting) -- 8.3.1 Opaque Cubes (Cuberilles) -- 8.3.2 Contour Tracing -- 8.3.3 Marching Cube -- 8.4 Direct Volume Rendering -- 8.4.1 Raycasting -- 8.4.2 Splatting -- 8.4.3 Shear-Warp -- 8.4.4 Maximum Intensity Projection -- 8.4.5 3D Texture Mapping Volume -- 8.5 Recent Advances in Volume Visualization.
8.5.1 Advances in Hardware (GPU)-Based Volume Rendering -- 8.5.1.1 The Need for GPU -- 8.5.1.2 Accelerators on the GPU -- 8.5.2 Advances in TFs -- 8.5.2.1 Image-Centric TFs -- 8.5.2.2 Data-Centric TFs -- 8.5.3 Generative Adversarial Networks(GANs) -- 8.6 Tools and Libraries for Volume Visualization -- 8.7 Conclusion and Future Directions -- References -- 9: A Review on the Evolution of Comprehensive Information for Digital Sliding of Pathology and Medical Image Segmentation -- 9.1 Introduction -- 9.1.1 Hurdles Encountered in Digitization of Pathological Information -- 9.2 Pathology Origins towards Whole Slide Imaging (WSI) -- 9.3 Digitalization of Pathology Imaging -- 9.4 Computational Analysis of Pathological Imaging -- 9.4.1 Demand for Scale -- 9.5 Management Infrastructure -- 9.5.1 WSI Acquisition, Management, and Exchange -- 9.5.2 Pathology Analytical Imaging Infrastructure -- 9.6 Investigation in Computerized Pathology Medical Imaging -- 9.6.1 Nephropathy Glomerulosclerosis: Integrative Using Active Contour -- 9.6.2 Mapping Molecules in the Tumor Microenvironment Using k-Means Clustering -- 9.6.3 Challenges Transversely Occurred in the Process -- 9.7 Conclusion -- References -- 10: Pathological Medical Image Segmentation: A Quick Review Based on Parametric Techniques -- 10.1 Introduction -- 10.1.1 Role and Flow of Medical Image Segmentation -- 10.1.2 Challenges in Medical Image Modalities for Segmentation -- 10.1.3 Architecture of Medical Image Modalities -- 10.1.3.1 MRI -- 10.1.3.2 Electron Microscopy -- 10.1.3.3 Computed Tomography -- 10.1.3.4 US -- 10.2 Medical Image Segmentation Techniques -- 10.2.1 Thresholding -- 10.2.2 Region Growing -- 10.2.3 Bayesian Approach -- 10.2.3.1 Maximum a Posteriori (MAP) -- 10.2.3.2 Markov Random Field (MRF) -- 10.2.3.3 Maximum Likelihood (ML) -- 10.2.3.4 Expectation Maximization (EM) -- 10.2.4 Clustering.
10.2.4.1 k-Means -- 10.2.4.2 Fuzzy C-Means -- 10.2.5 Deformable Methods -- 10.2.5.1 Parametric Deformable Models (Explicit) -- 10.2.5.2 Non-Parametric Models (Implicit) -- 10.2.6 Atlas-Guided Approaches -- 10.2.6.1 Atlas as Average Shape -- 10.2.6.2 Atlas as Individual Image -- 10.2.7 Edge-Based Approaches -- 10.2.8 Compression-Based Approaches -- 10.2.9 Other Techniques -- 10.3 Study and Conversation -- 10.4 Comparison of Medical Image Segmentation Methods with Experimental Analysis -- 10.5 Conclusions -- References -- Index.
Summary: The book aims to provide advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques, including examples from different medical conditions. 3D imaging in biomedical applications and pathological medical imaging is also reviewed.
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Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- 1: A Novel Stacked Model Ensemble for Improved TB Detection in Chest Radiographs -- 1.1 Introduction -- 1.2 Materials and Methods -- 1.2.1 Data Collection and Preprocessing -- 1.2.2 Proposal 1-Feature Extraction Using Local/Global Feature Descriptors and Classification Using SVM -- 1.2.3 Proposal 2-Feature Extraction and Classification Using a Customized CNN -- 1.2.4 Proposal 3-Feature Extraction Using Pre-Trained CNNs and Classification Using SVM -- 1.2.5 Proposal 4-Constructing Stacked Model Ensembles -- 1.3 Results and Discussion -- 1.4 Conclusion and Future Work -- Acknowledgments -- Conflict of Interest -- References -- 2: The Role of Artificial Intelligence (AI) in Medical Imaging: General Radiologic and Urologic Applications -- 2.1 Introduction to Artificial Intelligence (AI) -- 2.1.1 Terminology -- 2.1.2 Practical Costs -- 2.2 Artificial Intelligence in Medicine -- 2.3 Artificial Intelligence in Radiology -- 2.3.1 Extrinsic Factors to Image Interpretation -- 2.3.2 Intrinsic Factors to Image Quality -- 2.3.2.1 Geometry -- 2.3.2.2 Contrast -- 2.3.2.3 Background -- 2.3.3 Specific Technical Example of AI in Medical Imaging -- 2.4 Urologic Applications -- 2.5 Benefits vs. Disadvantages -- 2.6 Future Considerations -- References -- 3: Early Detection of Epileptic Seizures Based on Scalp EEG Signals -- 3.1 Introduction -- 3.2 Electroencephalogram -- 3.3 EEG Signal Processing -- 3.3.1 EEG Data Preprocessing -- 3.3.2 Feature Extraction -- 3.3.3 SVM Implementation -- 3.3.4 Performance Metrics -- 3.4 Results and Discussion -- 3.4.1 Comparison with Studies Following Similar Performance Metrics -- 3.4.2 Overall Performance -- 3.4.3 Using Epoch-Specific Values Instead of Event-Specific Values -- 3.5 Conclusion -- References.

4: Fractal Analysis in Histology Classification of Non-Small Cell Lung Cancer -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Image Analysis -- 4.2.2 Computation of Fractal Dimension -- 4.2.3 Extraction of Radiomics Features -- 4.2.4 Classification -- 4.2.5 Results -- 4.3 Conclusion -- References -- 5: Multi-Feature-Based Classification of Osteoarthritis in Knee Joint X-Ray Images -- 5.1 Introduction -- 5.2 Causes of OA -- 5.3 Levels of Knee OA -- 5.4 Proposed Work -- 5.5 Literature Survey -- 5.6 The Proposed Methodology -- 5.6.1 Noise Removal and Image Enhancement -- 5.6.2 Curvature-Based Feature Extraction Method -- 5.6.3 Segmentation of Image -- 5.6.4 Boundary Extraction -- 5.6.5 Edge Curvature Computation -- 5.6.6 Classification -- 5.6.7 Results and Discussion -- 5.6.7.1 Results for Abnormal Images -- 5.6.7.2 Results for Normal Images -- 5.6.7.3 Classification Results -- 5.7 Texture Analysis-Based Feature Extraction Method -- 5.7.1 Segmentation -- 5.7.2 Locating the Center of the Synovial Cavity -- 5.7.3 Feature Extraction -- 5.7.4 Classification -- 5.7.5 Results and Discussion -- 5.7.5.1 Results for Abnormal Image -- 5.7.5.2 Comparison -- 5.7.5.3 Failure Analysis -- 5.8 Conclusion -- Acknowledgment -- References -- 6: Detection and Classification of Non-Proliferative Diabetic Retinopathy Lesions -- 6.1 Introduction -- 6.2 Methodology -- 6.2.1 Preprocessing -- 6.2.1.1 RGB Color Separation -- 6.2.1.2 Mask Separation -- 6.2.1.3 Image Enhancement -- 6.2.1.4 Histogram Equalization -- 6.2.2 Removal of Optic Disc from Fundus Images -- 6.3 Detection of Microaneurysms -- 6.4 Detection of Hemorrhages -- 6.5 Detection of EXs -- 6.6 Extraction of Retinal Blood Vessels -- 6.7 Experimental Work -- 6.7.1 Extraction of Mask -- 6.7.2 Removal of OD -- 6.7.3 Detection of MAs -- 6.7.4 Detection of EXs -- 6.7.5 Detection of Hemorrhages.

6.7.6 Statistical Techniques on NPDR Lesions -- 6.7.6.1 Statistical Techniques on MAs -- 6.7.7 Statistical Techniques on EXs -- 6.7.8 Statistical Techniques on Hemorrhages -- 6.7.9 Statistical Techniques on Retinal Blood Vessels -- 6.7.10 Grading NPDR Lesions Using ANN -- 6.7.11 K-Means Clustering -- 6.7.12 Performance Measurement by Receiver Operating Characteristic Curve -- 6.8 Conclusion -- References -- 7: Segmentation and Analysis of CT Images for Bone Fracture Detection and Labeling -- 7.1 Introduction -- 7.2 Clinical Aspects -- 7.2.1 Anatomy of Long Bone -- 7.2.2 CT Imaging -- 7.2.3 Types of Fractures -- 7.3 Literature Survey -- 7.4 Proposed Methodology -- 7.4.1 Data Acquisition -- 7.4.2 Data Annotation -- 7.4.3 Unwanted Artifacts Removal -- 7.4.3.1 Histogram Stretching -- 7.4.4 Bone Region Extraction and Labeling -- 7.4.4.1 Seed Point Selection and Spreading -- 7.4.4.2 Threshold Value Definition -- 7.4.4.3 Unique Label Assignment -- 7.5 Results -- 7.5.1 Application to Real Patient-Specific Images -- 7.5.2 Clinical Ground Truth -- 7.5.3 Comparison with State-of-the-Art Methods -- 7.6 Conclusions -- Acknowledgment -- References -- 8: A Systematic Review of 3D Imaging in Biomedical Applications -- 8.1 Introduction -- 8.2 Volumetric Data -- 8.2.1 Data Acquisition -- 8.2.2 Volume Data -- 8.2.3 Grid Structures -- 8.2.4 Volume Visualization -- 8.2.5 Steps in Volume Visualization -- 8.2.5.1 Data Acquisition and Dimension Reconstruction -- 8.2.5.2 Data Preprocessing and Extraction -- 8.2.5.3 View Definition -- 8.3 Indirect Volume Rendering (Surface Fitting) -- 8.3.1 Opaque Cubes (Cuberilles) -- 8.3.2 Contour Tracing -- 8.3.3 Marching Cube -- 8.4 Direct Volume Rendering -- 8.4.1 Raycasting -- 8.4.2 Splatting -- 8.4.3 Shear-Warp -- 8.4.4 Maximum Intensity Projection -- 8.4.5 3D Texture Mapping Volume -- 8.5 Recent Advances in Volume Visualization.

8.5.1 Advances in Hardware (GPU)-Based Volume Rendering -- 8.5.1.1 The Need for GPU -- 8.5.1.2 Accelerators on the GPU -- 8.5.2 Advances in TFs -- 8.5.2.1 Image-Centric TFs -- 8.5.2.2 Data-Centric TFs -- 8.5.3 Generative Adversarial Networks(GANs) -- 8.6 Tools and Libraries for Volume Visualization -- 8.7 Conclusion and Future Directions -- References -- 9: A Review on the Evolution of Comprehensive Information for Digital Sliding of Pathology and Medical Image Segmentation -- 9.1 Introduction -- 9.1.1 Hurdles Encountered in Digitization of Pathological Information -- 9.2 Pathology Origins towards Whole Slide Imaging (WSI) -- 9.3 Digitalization of Pathology Imaging -- 9.4 Computational Analysis of Pathological Imaging -- 9.4.1 Demand for Scale -- 9.5 Management Infrastructure -- 9.5.1 WSI Acquisition, Management, and Exchange -- 9.5.2 Pathology Analytical Imaging Infrastructure -- 9.6 Investigation in Computerized Pathology Medical Imaging -- 9.6.1 Nephropathy Glomerulosclerosis: Integrative Using Active Contour -- 9.6.2 Mapping Molecules in the Tumor Microenvironment Using k-Means Clustering -- 9.6.3 Challenges Transversely Occurred in the Process -- 9.7 Conclusion -- References -- 10: Pathological Medical Image Segmentation: A Quick Review Based on Parametric Techniques -- 10.1 Introduction -- 10.1.1 Role and Flow of Medical Image Segmentation -- 10.1.2 Challenges in Medical Image Modalities for Segmentation -- 10.1.3 Architecture of Medical Image Modalities -- 10.1.3.1 MRI -- 10.1.3.2 Electron Microscopy -- 10.1.3.3 Computed Tomography -- 10.1.3.4 US -- 10.2 Medical Image Segmentation Techniques -- 10.2.1 Thresholding -- 10.2.2 Region Growing -- 10.2.3 Bayesian Approach -- 10.2.3.1 Maximum a Posteriori (MAP) -- 10.2.3.2 Markov Random Field (MRF) -- 10.2.3.3 Maximum Likelihood (ML) -- 10.2.3.4 Expectation Maximization (EM) -- 10.2.4 Clustering.

10.2.4.1 k-Means -- 10.2.4.2 Fuzzy C-Means -- 10.2.5 Deformable Methods -- 10.2.5.1 Parametric Deformable Models (Explicit) -- 10.2.5.2 Non-Parametric Models (Implicit) -- 10.2.6 Atlas-Guided Approaches -- 10.2.6.1 Atlas as Average Shape -- 10.2.6.2 Atlas as Individual Image -- 10.2.7 Edge-Based Approaches -- 10.2.8 Compression-Based Approaches -- 10.2.9 Other Techniques -- 10.3 Study and Conversation -- 10.4 Comparison of Medical Image Segmentation Methods with Experimental Analysis -- 10.5 Conclusions -- References -- Index.

The book aims to provide advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques, including examples from different medical conditions. 3D imaging in biomedical applications and pathological medical imaging is also reviewed.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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