TY  - BOOK
AU  - Santosh,K.C.
AU  - Antani,Sameer
AU  - Guru,D.S.
AU  - Dey,Nilanjan
TI  - Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques
SN  - 9780429642494
AV  - RC78.7.D53 .M435 2020 
U1  - 616.0754 
PY  - 2019///
CY  - Milton
PB  - Taylor & Francis Group
KW  - Diagnostic imaging
KW  - Electronic books
N1  - 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
N2  - 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
UR  - https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=5855476
ER  -