{"product_id":"biomedical-image-understanding-9781118715154","title":"Biomedical Image Understanding","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eOffers a comprehensive guide to understanding and interpreting digital images in medical and functional applications. This book focuses on image understanding and semantic interpretation, with clear introductions to related concepts and in-depth theoretical analysis. It is suitable for the reader interested in biomedical image understanding.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eList of Contributors xv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface xix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcronyms xxiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I INTRODUCTION 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview of Biomedical Image Understanding Methods 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWei Xiong, Jierong Cheng, Ying Gu, Shimiao Li and Joo Hwee Lim\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Segmentation and Object Detection 5\u003c\/p\u003e \u003cp\u003e1.1.1 Methods Based on Image Processing Techniques 6\u003c\/p\u003e \u003cp\u003e1.1.2 Methods Using Pattern Recognition and Machine Learning Algorithms 7\u003c\/p\u003e \u003cp\u003e1.1.3 Model and Atlas-Based Segmentation 8\u003c\/p\u003e \u003cp\u003e1.1.4 Multispectral Segmentation 9\u003c\/p\u003e \u003cp\u003e1.1.5 User Interactions in Interactive Segmentation Methods 10\u003c\/p\u003e \u003cp\u003e1.1.6 Frontiers of Biomedical Image Segmentation 11\u003c\/p\u003e \u003cp\u003e1.2 Registration 11\u003c\/p\u003e \u003cp\u003e1.2.1 Taxonomy of Registration Methods 12\u003c\/p\u003e \u003cp\u003e1.2.2 Frontiers of Registration for Biomedical Image Understanding 15\u003c\/p\u003e \u003cp\u003e1.3 Object Tracking 16\u003c\/p\u003e \u003cp\u003e1.3.1 Object Representation 17\u003c\/p\u003e \u003cp\u003e1.3.2 Feature Selection for Tracking 18\u003c\/p\u003e \u003cp\u003e1.3.3 Object Tracking Technique 19\u003c\/p\u003e \u003cp\u003e1.3.4 Frontiers of Object Tracking 19\u003c\/p\u003e \u003cp\u003e1.4 Classification 20\u003c\/p\u003e \u003cp\u003e1.4.1 Feature Extraction and Feature Selection 21\u003c\/p\u003e \u003cp\u003e1.4.2 Classifiers 22\u003c\/p\u003e \u003cp\u003e1.4.3 Unsupervised Classification 23\u003c\/p\u003e \u003cp\u003e1.4.4 Classifier Combination 24\u003c\/p\u003e \u003cp\u003e1.4.5 Frontiers of Pattern Classification for Biomedical Image Understanding 25\u003c\/p\u003e \u003cp\u003e1.5 Knowledge-Based Systems 26\u003c\/p\u003e \u003cp\u003e1.5.1 Semantic Interpretation and Knowledge-Based Systems 26\u003c\/p\u003e \u003cp\u003e1.5.2 Knowledge-Based Vision Systems 27\u003c\/p\u003e \u003cp\u003e1.5.3 Knowledge-Based Vision Systems in Biomedical Image Analysis 28\u003c\/p\u003e \u003cp\u003e1.5.4 Frontiers of Knowledge-Based Systems 29\u003c\/p\u003e \u003cp\u003eReferences 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePARTII SEGMENTATION AND OBJECT DETECTION 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Medical Image Segmentation and its Application in Cardiac MRI 49\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDong Wei, Chao Li, and Ying Sun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 50\u003c\/p\u003e \u003cp\u003e2.2 Background 51\u003c\/p\u003e \u003cp\u003e2.2.1 Active Contour Models 51\u003c\/p\u003e \u003cp\u003e2.2.2 Parametric and Nonparametric Contour Representation 52\u003c\/p\u003e \u003cp\u003e2.2.3 Graph-Based Image Segmentation 53\u003c\/p\u003e \u003cp\u003e2.2.4 Summary 54\u003c\/p\u003e \u003cp\u003e2.3 Parametric Active Contours – The Snakes 54\u003c\/p\u003e \u003cp\u003e2.3.1 The Internal Spline Energy \u003ci\u003eE\u003c\/i\u003eint 54\u003c\/p\u003e \u003cp\u003e2.3.2 The Image-Derived Energy \u003ci\u003eE\u003c\/i\u003eimg 55\u003c\/p\u003e \u003cp\u003e2.3.3 The External Control Energy \u003ci\u003eE\u003c\/i\u003econ 55\u003c\/p\u003e \u003cp\u003e2.3.4 Extension of Snakes and Summary of Parametric Active Contours 57\u003c\/p\u003e \u003cp\u003e2.4 Geometric Active Contours – The Level Sets 58\u003c\/p\u003e \u003cp\u003e2.4.1 Variational Level Set Methods 58\u003c\/p\u003e \u003cp\u003e2.4.2 Region-Based Variational Level Set Methods 60\u003c\/p\u003e \u003cp\u003e2.4.3 Summary of Level Set Methods 64\u003c\/p\u003e \u003cp\u003e2.5 Graph-Based Methods – The Graph Cuts 65\u003c\/p\u003e \u003cp\u003e2.5.1 Basic Graph Cuts Formulation 65\u003c\/p\u003e \u003cp\u003e2.5.2 Patch-Based Graph Cuts 66\u003c\/p\u003e \u003cp\u003e2.5.3 An Example of Graph Cuts 68\u003c\/p\u003e \u003cp\u003e2.5.4 Summary of Graph Cut Methods 72\u003c\/p\u003e \u003cp\u003e2.6 Case Study: Cardiac Image Segmentation Using A Dual Level Sets Model 73\u003c\/p\u003e \u003cp\u003e2.6.1 Introduction 73\u003c\/p\u003e \u003cp\u003e2.6.2 Method 74\u003c\/p\u003e \u003cp\u003e2.6.3 Experimental Results 79\u003c\/p\u003e \u003cp\u003e2.6.4 Conclusion of the Case Study 81\u003c\/p\u003e \u003cp\u003e2.7 Conclusion and Near-Future Trends 81\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Morphometric Measurements of the Retinal Vasculature in Fundus Images With Vampire 91\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEmanuele Trucco, Andrea Giachetti, Lucia Ballerini, Devanjali Relan, Alessandro Cavinato, and Tom Macgillivray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 92\u003c\/p\u003e \u003cp\u003e3.2 Assessing Vessel Width 93\u003c\/p\u003e \u003cp\u003e3.2.1 Previous Work 93\u003c\/p\u003e \u003cp\u003e3.2.2 Our Method 94\u003c\/p\u003e \u003cp\u003e3.2.3 Results 95\u003c\/p\u003e \u003cp\u003e3.2.4 Discussion 96\u003c\/p\u003e \u003cp\u003e3.3 Artery or Vein? 98\u003c\/p\u003e \u003cp\u003e3.3.1 Previous Work 98\u003c\/p\u003e \u003cp\u003e3.3.2 Our Solution 99\u003c\/p\u003e \u003cp\u003e3.3.3 Results 101\u003c\/p\u003e \u003cp\u003e3.3.4 Discussion 103\u003c\/p\u003e \u003cp\u003e3.4 Are My Program’s Measurements Accurate? 104\u003c\/p\u003e \u003cp\u003e3.4.1 Discussion 106\u003c\/p\u003e \u003cp\u003eReferences 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Analyzing Cell and Tissue Morphologies Using Pattern Recognition Algorithms 113\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHwee Kuan Lee, Yan Nei Law, Chao-Hui Huang, and Choon Kong Yap \u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction, 113\u003c\/p\u003e \u003cp\u003e4.2 Texture Segmentation of Endometrial Images Using the Subspace Mumford–Shah Model 115\u003c\/p\u003e \u003cp\u003e4.2.1 Subspace Mumford–Shah Segmentation Model 116\u003c\/p\u003e \u003cp\u003e4.2.2 Feature Weights 118\u003c\/p\u003e \u003cp\u003e4.2.3 Once-and-For-All Approach 119\u003c\/p\u003e \u003cp\u003e4.2.4 Results 119\u003c\/p\u003e \u003cp\u003e4.3 Spot Clustering for Detection of Mutants in Keratinocytes 120\u003c\/p\u003e \u003cp\u003e4.3.1 Image Analysis Framework 123\u003c\/p\u003e \u003cp\u003e4.3.2 Results 124\u003c\/p\u003e \u003cp\u003e4.4 Cells and Nuclei Detection 124\u003c\/p\u003e \u003cp\u003e4.4.1 Model 125\u003c\/p\u003e \u003cp\u003e4.4.2 Neural Cells and Breast Cancer Cells Data 127\u003c\/p\u003e \u003cp\u003e4.4.3 Performance Evaluation 127\u003c\/p\u003e \u003cp\u003e4.4.4 Robustness Study 127\u003c\/p\u003e \u003cp\u003e4.4.5 Results 128\u003c\/p\u003e \u003cp\u003e4.5 Geometric Regional Graph Spectral Feature 134\u003c\/p\u003e \u003cp\u003e4.5.1 Conversion of Image Patches into Region Signatures 134\u003c\/p\u003e \u003cp\u003e4.5.2 Comparing Region Signatures 135\u003c\/p\u003e \u003cp\u003e4.5.3 Classification of Region Signatures 136\u003c\/p\u003e \u003cp\u003e4.5.4 Random Masking and Object Detection 136\u003c\/p\u003e \u003cp\u003e4.5.5 Results 137\u003c\/p\u003e \u003cp\u003e4.6 Mitotic Cells in the H\u0026amp;E Histopathological Images of Breast Cancer Carcinoma 138\u003c\/p\u003e \u003cp\u003e4.6.1 Mitotic Index Estimation 139\u003c\/p\u003e \u003cp\u003e4.6.2 Mitotic Candidate Selection 140\u003c\/p\u003e \u003cp\u003e4.6.3 Exclusive Independent Component Analysis (XICA) 140\u003c\/p\u003e \u003cp\u003e4.6.4 Classification Using Sparse Representation 143\u003c\/p\u003e \u003cp\u003e4.6.5 Training and Testing Over Channels 144\u003c\/p\u003e \u003cp\u003e4.6.6 Results 146\u003c\/p\u003e \u003cp\u003e4.7 Conclusions 147\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePARTIII REGISTRATION AND MATCHING 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 3D Nonrigid Image Registration by Parzen-Window-Based Normalized Mutual Information and its Application on Mr-Guided Microwave Thermocoagulation of Liver Tumors 155\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRui Xu, Yen-Wei Chen, Shigehiro Morikawa, and Yoshimasa Kurumi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 155\u003c\/p\u003e \u003cp\u003e5.2 Parzen-Window-Based Normalized Mutual Information 157\u003c\/p\u003e \u003cp\u003e5.2.1 Definition of Parzen-Window Method 157\u003c\/p\u003e \u003cp\u003e5.2.2 Parzen-Window-Based Estimation of Joint Histogram 158\u003c\/p\u003e \u003cp\u003e5.2.3 Normalized Mutual Information and its Derivative 160\u003c\/p\u003e \u003cp\u003e5.3 Analysis of Kernel Selection 163\u003c\/p\u003e \u003cp\u003e5.3.1 The Designed Kernel 163\u003c\/p\u003e \u003cp\u003e5.3.2 Comparison in Theory 167\u003c\/p\u003e \u003cp\u003e5.3.3 Comparison by Experiments 170\u003c\/p\u003e \u003cp\u003e5.4 Application on MR-Guided Microwave Thermocoagulation of Liver Tumors 174\u003c\/p\u003e \u003cp\u003e5.4.1 Introduction of MR-Guided Microwave Thermocoagulation of Liver Tumors 174\u003c\/p\u003e \u003cp\u003e5.4.2 Nonrigid Registration by Parzen-Window-Based Mutual Information 175\u003c\/p\u003e \u003cp\u003e5.4.3 Evaluation on Phantom Data 177\u003c\/p\u003e \u003cp\u003e5.4.4 Evaluation on Clinical Cases 180\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 185\u003c\/p\u003e \u003cp\u003eAcknowledgements 186\u003c\/p\u003e \u003cp\u003eReferences 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 2D\/3D Image Registration For Endovascular Abdominal Aortic Aneurysm (AAA) Repair 189\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShun Miao and Rui Liao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 189\u003c\/p\u003e \u003cp\u003e6.2 Background 190\u003c\/p\u003e \u003cp\u003e6.2.1 Image Modalities 190\u003c\/p\u003e \u003cp\u003e6.2.2 2D\/3D Registration Framework 192\u003c\/p\u003e \u003cp\u003e6.2.3 Feature-Based Registration 194\u003c\/p\u003e \u003cp\u003e6.2.4 Intensity-Based Registration 196\u003c\/p\u003e \u003cp\u003e6.2.5 Number of Imaging Planes 197\u003c\/p\u003e \u003cp\u003e6.2.6 2D\/3D Registration for Endovascular AAA Repair 198\u003c\/p\u003e \u003cp\u003e6.3 Smart Utilization of Two X-Ray Images for Rigid-Body 2D\/3D Registration 199\u003c\/p\u003e \u003cp\u003e6.3.1 2D\/3D Registration: Challenges in EVAR 199\u003c\/p\u003e \u003cp\u003e6.3.2 3D Image Processing and DRR Generation 202\u003c\/p\u003e \u003cp\u003e6.3.3 2D Image Processing 203\u003c\/p\u003e \u003cp\u003e6.3.4 Similarity Measure 205\u003c\/p\u003e \u003cp\u003e6.3.5 Optimization 207\u003c\/p\u003e \u003cp\u003e6.3.6 Validation 210\u003c\/p\u003e \u003cp\u003e6.4 Deformable 2D\/3D Registration 211\u003c\/p\u003e \u003cp\u003e6.4.1 Problem Formulation 212\u003c\/p\u003e \u003cp\u003e6.4.2 Graph-Based Difference Measure 213\u003c\/p\u003e \u003cp\u003e6.4.3 Length Preserving Term 215\u003c\/p\u003e \u003cp\u003e6.4.4 Smoothness Term 215\u003c\/p\u003e \u003cp\u003e6.4.5 Optimization 216\u003c\/p\u003e \u003cp\u003e6.4.6 Validation 217\u003c\/p\u003e \u003cp\u003e6.5 Visual Check of Patient Movement Using Pelvis Boundary Detection 220\u003c\/p\u003e \u003cp\u003e6.6 Discussion and Conclusion 222\u003c\/p\u003e \u003cp\u003eReferences 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePARTIV OBJECT TRACKING 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Motion Tracking in Medical Images 231\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChuqing Cao, Chao Li, and Ying Sun\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 232\u003c\/p\u003e \u003cp\u003e7.1.1 Point-Based Tracking 233\u003c\/p\u003e \u003cp\u003e7.1.2 Silhouette-Based Tracking 233\u003c\/p\u003e \u003cp\u003e7.1.3 Kernel-Based Tracking 233\u003c\/p\u003e \u003cp\u003e7.2 Background 234\u003c\/p\u003e \u003cp\u003e7.2.1 Point-Based Tracking 234\u003c\/p\u003e \u003cp\u003e7.2.2 Silhouette-Based Tracking 236\u003c\/p\u003e \u003cp\u003e7.2.3 Kernel-Based Tracking 237\u003c\/p\u003e \u003cp\u003e7.2.4 Summary 238\u003c\/p\u003e \u003cp\u003e7.3 Bayesian Tracking Methods 238\u003c\/p\u003e \u003cp\u003e7.3.1 Kalman Filters 239\u003c\/p\u003e \u003cp\u003e7.3.2 Particle Filters 240\u003c\/p\u003e \u003cp\u003e7.3.3 Summary of Bayesian Tracking Methods 241\u003c\/p\u003e \u003cp\u003e7.4 Deformable Models 241\u003c\/p\u003e \u003cp\u003e7.4.1 Mathematical Foundations of Deformable Models 241\u003c\/p\u003e \u003cp\u003e7.4.2 Energy-Minimizing Deformable Models 242\u003c\/p\u003e \u003cp\u003e7.4.3 Probabilistic Deformable Models 244\u003c\/p\u003e \u003cp\u003e7.4.4 Summary of Deformable Models 245\u003c\/p\u003e \u003cp\u003e7.5 Motion Tracking Based on the Harmonic Phase Algorithm 246\u003c\/p\u003e \u003cp\u003e7.5.1 HARP Imaging 246\u003c\/p\u003e \u003cp\u003e7.5.2 HARP Tracking 248\u003c\/p\u003e \u003cp\u003e7.5.3 Summary 249\u003c\/p\u003e \u003cp\u003e7.6 Case Study: Pseudo Ground Truth-Based Nonrigid Registration of MRI for Tracking the Cardiac Motion 250\u003c\/p\u003e \u003cp\u003e7.6.1 Data Fidelity Term 251\u003c\/p\u003e \u003cp\u003e7.6.2 Spatial Smoothness Constraint 252\u003c\/p\u003e \u003cp\u003e7.6.3 Temporal Smoothness Constraint 253\u003c\/p\u003e \u003cp\u003e7.6.4 Energy Minimization 254\u003c\/p\u003e \u003cp\u003e7.6.5 Preliminary Results 255\u003c\/p\u003e \u003cp\u003e7.6.6 Nonrigid Registration of Myocardial Perfusion MRI 255\u003c\/p\u003e \u003cp\u003e7.6.7 Experimental Results 259\u003c\/p\u003e \u003cp\u003e7.7 Discussion 264\u003c\/p\u003e \u003cp\u003e7.8 Conclusion and Near-Future Trends 265\u003c\/p\u003e \u003cp\u003eReferences 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePARTV CLASSIFICATION 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Blood Smear Analysis, Malaria Infection Detection, and Grading from Blood Cell Images 277\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWei Xiong, Sim-Heng Ong, Joo-Hwee Lim, Jierong Cheng, and Ying Gu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 278\u003c\/p\u003e \u003cp\u003e8.2 Pattern Classification Techniques 282\u003c\/p\u003e \u003cp\u003e8.2.1 Supervised and Nonsupervised Learning 282\u003c\/p\u003e \u003cp\u003e8.2.2 Bayesian Decision Theory 283\u003c\/p\u003e \u003cp\u003e8.2.3 Clustering 284\u003c\/p\u003e \u003cp\u003e8.2.4 Support Vector Machines 286\u003c\/p\u003e \u003cp\u003e8.3 GWA Detection 287\u003c\/p\u003e \u003cp\u003e8.3.1 Image Analysis 288\u003c\/p\u003e \u003cp\u003e8.3.2 Association between the Object Area and the Number of Cells Per Object 289\u003c\/p\u003e \u003cp\u003e8.3.3 Clump Splitting 291\u003c\/p\u003e \u003cp\u003e8.3.4 Clump Characterization 293\u003c\/p\u003e \u003cp\u003e8.3.5 Classification 295\u003c\/p\u003e \u003cp\u003e8.4 Dual-Model-Guided Image Segmentation and Recognition 295\u003c\/p\u003e \u003cp\u003e8.4.1 Related Work 296\u003c\/p\u003e \u003cp\u003e8.4.2 Strategies and Object Functions 297\u003c\/p\u003e \u003cp\u003e8.4.3 Endpoint Adjacency Map Construction and Edge Linking 299\u003c\/p\u003e \u003cp\u003e8.4.4 Parsing Contours and Their Convex Hulls 300\u003c\/p\u003e \u003cp\u003e8.4.5 A Recursive and Greedy Splitting Approach 301\u003c\/p\u003e \u003cp\u003e8.4.6 Incremental Model Updating and Bayesian Decision 301\u003c\/p\u003e \u003cp\u003e8.5 Infection Detection and Staging 302\u003c\/p\u003e \u003cp\u003e8.5.1 Related Work 302\u003c\/p\u003e \u003cp\u003e8.5.2 Methodology 303\u003c\/p\u003e \u003cp\u003e8.6 Experimental Results 305\u003c\/p\u003e \u003cp\u003e8.6.1 GWA Classification 305\u003c\/p\u003e \u003cp\u003e8.6.2 RBC Segmentation 310\u003c\/p\u003e \u003cp\u003e8.6.3 RBC Classification 315\u003c\/p\u003e \u003cp\u003e8.7 Summary 320\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Liver Tumor Segmentation Using SVM Framework and Pathology Characterization Using Content-Based Image Retrieval 325\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJiayin Zhou, Yanling Chi, Weimin Huang, Wei Xiong, Wenyu Chen, Jimin Liu, and Sudhakar K. Venkatesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 325\u003c\/p\u003e \u003cp\u003e9.2 Liver Tumor Segmentation Under a Hybrid SVM Framework 327\u003c\/p\u003e \u003cp\u003e9.2.1 Fundamentals of SVM for Classification 327\u003c\/p\u003e \u003cp\u003e9.2.2 SVM Framework for Liver Tumor Segmentation and the Problems 330\u003c\/p\u003e \u003cp\u003e9.2.3 A Three-Stage Hybrid SVM Scheme for Liver Tumor Segmentation 331\u003c\/p\u003e \u003cp\u003e9.2.4 Experiment 334\u003c\/p\u003e \u003cp\u003e9.2.5 Evaluation Metrics 335\u003c\/p\u003e \u003cp\u003e9.2.6 Results 336\u003c\/p\u003e \u003cp\u003e9.3 Liver Tumor Characterization by Content-Based Image Retrieval 338\u003c\/p\u003e \u003cp\u003e9.3.1 Existing Work and the Rationale of Using CBIR 339\u003c\/p\u003e \u003cp\u003e9.3.2 Methodology Overview and Preprocessing 340\u003c\/p\u003e \u003cp\u003e9.3.3 Tumor Feature Representation 341\u003c\/p\u003e \u003cp\u003e9.3.4 Similarity Query and Tumor Pathological Type Prediction 343\u003c\/p\u003e \u003cp\u003e9.3.5 Experiment 345\u003c\/p\u003e \u003cp\u003e9.3.6 Results 346\u003c\/p\u003e \u003cp\u003e9.4 Discussion 351\u003c\/p\u003e \u003cp\u003e9.4.1 About Liver Tumor Segmentation Using Machine Learning 351\u003c\/p\u003e \u003cp\u003e9.4.2 About Liver Tumor Characterization Using CBIR 353\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 356\u003c\/p\u003e \u003cp\u003eReferences 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Benchmarking Lymph Node Metastasis Classification for Gastric Cancer Staging 361\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSu Zhang, Chao Li, Shuheng Zhang, Lifang Pang, and Huan Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 362\u003c\/p\u003e \u003cp\u003e10.1.1 Introduction of GSI-CT 363\u003c\/p\u003e \u003cp\u003e10.1.2 Imaging Findings of Gastric Cancer 366\u003c\/p\u003e \u003cp\u003e10.2 Related Feature Selection, Metric Learning, and Classification Methods 367\u003c\/p\u003e \u003cp\u003e10.2.1 Feature Extraction 367\u003c\/p\u003e \u003cp\u003e10.2.2 KNN 367\u003c\/p\u003e \u003cp\u003e10.2.3 Feature Selection 369\u003c\/p\u003e \u003cp\u003e10.2.4 AdaBoost and EAdaBoost Algorithms 374\u003c\/p\u003e \u003cp\u003e10.3 Preprocessing Method for GSI-CT Data 377\u003c\/p\u003e \u003cp\u003e10.3.1 Data Acquisition for GSI-CT Data 377\u003c\/p\u003e \u003cp\u003e10.3.2 Univariate Analysis 378\u003c\/p\u003e \u003cp\u003e10.4 Classification Results For GSI-CT Data of Gastric Cancer 379\u003c\/p\u003e \u003cp\u003e10.4.1 Experimental Results of mRMR-KNN 379\u003c\/p\u003e \u003cp\u003e10.4.2 Experimental Results of SFS-KNN 383\u003c\/p\u003e \u003cp\u003e10.4.3 Experimental Results of Metric Learning 385\u003c\/p\u003e \u003cp\u003e10.4.4 Experiments Results of AdaBoost and EAdaBoost 385\u003c\/p\u003e \u003cp\u003e10.4.5 Experiment Analysis 388\u003c\/p\u003e \u003cp\u003e10.5 Conclusion and Future Work 388\u003c\/p\u003e \u003cp\u003eAcknowledgment 388\u003c\/p\u003e \u003cp\u003eReferences 388\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePARTVI KNOWLEDGE-BASED SYSTEMS 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The Use of Knowledge in Biomedical Image Analysis 395\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFlorence Cloppet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 395\u003c\/p\u003e \u003cp\u003e11.2 Data, Information, and Knowledge? 397\u003c\/p\u003e \u003cp\u003e11.2.1 Data Versus Information 397\u003c\/p\u003e \u003cp\u003e11.2.2 Knowledge Versus Information 398\u003c\/p\u003e \u003cp\u003e11.3 What Kind of Information\/Knowledge Can be Introduced? 399\u003c\/p\u003e \u003cp\u003e11.4 How to Introduce Information in Computer Vision Systems? 400\u003c\/p\u003e \u003cp\u003e11.4.1 Nature of Prior Information\/Knowledge 402\u003c\/p\u003e \u003cp\u003e11.4.2 Frameworks Allowing Prior Information Introduction 408\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 418\u003c\/p\u003e \u003cp\u003eReferences 418\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Active Shape Model for Contour Detection of Anatomical Structure 429\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHuiqi Li and Qing Nie\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 429\u003c\/p\u003e \u003cp\u003e12.2 Background 430\u003c\/p\u003e \u003cp\u003e12.2.1 Free-Form Deformable Models 430\u003c\/p\u003e \u003cp\u003e12.2.2 Parametrically Deformable Models 432\u003c\/p\u003e \u003cp\u003e12.3 Methodology 434\u003c\/p\u003e \u003cp\u003e12.3.1 Point Distribution Model 434\u003c\/p\u003e \u003cp\u003e12.3.2 Active Shape Model (ASM) 436\u003c\/p\u003e \u003cp\u003e12.3.3 A Modified ASM 438\u003c\/p\u003e \u003cp\u003e12.4 Applications 440\u003c\/p\u003e \u003cp\u003e12.4.1 Boundary Detection of Optic Disk 440\u003c\/p\u003e \u003cp\u003e12.4.2 Lens Structure Detection 450\u003c\/p\u003e \u003cp\u003e12.5 Summary 456\u003c\/p\u003e \u003cp\u003eAcknowledgment 457\u003c\/p\u003e \u003cp\u003eReferences 457\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 463\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406910038359,"sku":"9781118715154","price":121.46,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118715154.jpg?v=1730497527","url":"https:\/\/bookcurl.com\/products\/biomedical-image-understanding-9781118715154","provider":"Book Curl","version":"1.0","type":"link"}