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El. knyga: Biomedical Image Understanding: Methods and Applications

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A comprehensive guide to understanding and interpreting digital images in medical and functional applications

Biomedical Image Understanding focuses on image understanding and semantic interpretation, with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications. It covers image processing, image filtering, enhancement, de-noising, restoration, and reconstruction; image segmentation and feature extraction; registration; clustering, pattern classification, and data fusion.

With contributions from experts in China, France, Italy, Japan, Singapore, the United Kingdom, and the United States, Biomedical Image Understanding: 





Addresses motion tracking and knowledge-based systems, two areas which are not covered extensively elsewhere in a biomedical context Describes important clinical applications, such as virtual colonoscopy, ocular disease diagnosis, and liver tumor detection Contains twelve self-contained chapters, each with an introduction to basic concepts, principles, and methods, and a case study or application

With over 150 diagrams and illustrations, this bookis an essential resource for the reader interested in rapidly advancing research and applications in biomedical image understanding.
List of Contributors xv
Preface xix
Acronyms xxiii
Part I Introduction 1(46)
1 Overview of Biomedical Image Understanding Methods
3(44)
Wei Xiong
Jierong Cheng
Ying Gu
Shimiao Li
Joo Hwee Lim
1.1 Segmentation and Object Detection,
5(6)
1.1.1 Methods Based on Image Processing Techniques,
6(1)
1.1.2 Methods Using Pattern Recognition and Machine Learning Algorithms,
7(1)
1.1.3 Model and Atlas-Based Segmentation,
8(1)
1.1.4 Multispectral Segmentation,
9(1)
1.1.5 User Interactions in Interactive Segmentation Methods,
10(1)
1.1.6 Frontiers of Biomedical Image Segmentation,
11(1)
1.2 Registration,
11(5)
1.2.1 Taxonomy of Registration Methods,
12(3)
1.2.2 Frontiers of Registration for Biomedical Image Understanding,
15(1)
1.3 Object Tracking,
16(4)
1.3.1 Object Representation,
17
1.3.2 Feature Selection for Tracking, I
8(11)
1.3.3 Object Tracking Technique,
19(1)
1.3.4 Frontiers of Object Tracking,
19(1)
1.4 Classification,
20(6)
1.4.1 Feature Extraction and Feature Selection,
21(1)
1.4.2 Classifiers,
22(1)
1.4.3 Unsupervised Classification,
23(1)
1.4.4 Classifier Combination,
24(1)
1.4.5 Frontiers of Pattern Classification for Biomedical Image Understanding,
25(1)
1.5 Knowledge-Based Systems,
26(3)
1.5.1 Semantic Interpretation and Knowledge-Based Systems,
26(1)
1.5.2 Knowledge-Based Vision Systems,
27(1)
1.5.3 Knowledge-Based Vision Systems in Biomedical Image Analysis,
28(1)
1.5.4 Frontiers of Knowledge-Based Systems,
29(1)
References,
29(18)
Part II Segmentation And Object Detection 47(106)
2 Medical Image Segmentation and its Application in Cardiac MRI
49(42)
Dong Wei
Chao Li
Ying Sun
2.1 Introduction,
50(1)
2.2 Background,
51(3)
2.2.1 Active Contour Models,
51(1)
2.2.2 Parametric and Nonparametric Contour Representation,
52(1)
2.2.3 Graph-Based Image Segmentation,
53(1)
2.2.4 Summary,
54(1)
2.3 Parametric Active Contours - The Snakes,
54(4)
2.3.1 The Internal Spline Energy Eint,
54(1)
2.3.2 The Image-Derived Energy Eimg,
55(1)
2.3.3 The External Control Energy Econ,
55(2)
2.3.4 Extension of Snakes and Summary of Parametric Active Contours,
57(1)
2.4 Geometric Active Contours - The Level Sets,
58(7)
2.4.1 Variational Level Set Methods,
58(2)
2.4.2 Region-Based Variational Level Set Methods,
60(4)
2.4.3 Summary of Level Set Methods,
64(1)
2.5 Graph-Based Methods - The Graph Cuts,
65(8)
2.5.1 Basic Graph Cuts Formulation,
65(1)
2.5.2 Patch-Based Graph Cuts,
66(2)
2.5.3 An Example of Graph Cuts,
68(4)
2.5.4 Summary of Graph Cut Methods,
72(1)
2.6 Case Study: Cardiac Image Segmentation Using A Dual Level Sets Model,
73(8)
2.6.1 Introduction,
73(1)
2.6.2 Method,
74(5)
2.6.3 Experimental Results,
79(2)
2.6.4 Conclusion of the Case Study,
81(1)
2.7 Conclusion and Near-Future Trends,
81(2)
References,
83(8)
3 Morphometric Measurements of the Retinal Vasculature in Fundus Images With Vampire
91(22)
Emanuele Trucco
Andrea Giachetti
Lucia Ballerini
Devanjali Relan
Alessandro Cavinato
Tom Macgillivray
3.1 Introduction,
92(1)
3.2 Assessing Vessel Width,
93(5)
3.2.1 Previous Work,
93(1)
3.2.2 Our Method,
94(1)
3.2.3 Results,
95(1)
3.2.4 Discussion,
96(2)
3.3 Artery or Vein?
98(6)
3.3.1 Previous Work,
98(1)
3.3.2 Our Solution,
99(2)
3.3.3 Results,
101(2)
3.3.4 Discussion,
103(1)
3.4 Are My Program's Measurements Accurate?
104(3)
3.4.1 Discussion,
106(1)
References,
107(6)
4 Analyzing Cell and Tissue Morphologies Using Pattern Recognition Algorithms
113(40)
Hwee Kuan Lee
Yan Nei Law
Chao-Hui Huang
Choon Kong Yap
4.1 Introduction,
113(2)
4.2 Texture Segmentation of Endometrial Images Using the Subspace Mumford-Shah Model,
115(5)
4.2.1 Subspace Mumford-Shah Segmentation Model,
116(2)
4.2.2 Feature Weights,
118(1)
4.2.3 Once-and-For-All Approach,
119(1)
4.2.4 Results,
119(1)
4.3 Spot Clustering for Detection of Mutants in Keratinocytes,
120(4)
4.3.1 Image Analysis Framework,
123(1)
4.3.2 Results,
124(1)
4.4 Cells and Nuclei Detection,
124(10)
4.4.1 Model,
125(2)
4.4.2 Neural Cells and Breast Cancer Cells Data,
127(1)
4.4.3 Performance Evaluation,
127(1)
4.4.4 Robustness Study,
127(1)
4.4.5 Results,
128(6)
4.5 Geometric Regional Graph Spectral Feature,
134(4)
4.5.1 Conversion of Image Patches into Region Signatures,
134(1)
4.5.2 Comparing Region Signatures,
135(1)
4.5.3 Classification of Region Signatures,
136(1)
4.5.4 Random Masking and Object Detection,
136(1)
4.5.5 Results,
137(1)
4.6 Mitotic Cells in the H&E Histopathological Images of Breast Cancer Carcinoma,
138(9)
4.6.1 Mitotic Index Estimation,
139(1)
4.6.2 Mitotic Candidate Selection,
140(1)
4.6.3 Exclusive Independent Component Analysis (XICA),
140(3)
4.6.4 Classification Using Sparse Representation,
143(1)
4.6.5 Training and Testing Over Channels,
144(2)
4.6.6 Results,
146(1)
4.7 Conclusions,
147(1)
References,
147(6)
Part III Registration And Matching 153(76)
5 3D Nonrigid Image Registration by Parzen-Window-Based Normalized Mutual Information and its Application on Mr-Guided Microwave Thermocoagulation of Liver Tumors
155(34)
Rui Xu
Yen-Wei Chen
Shigehiro Morikawa
Yoshimasa Kurumi
5.1 Introduction,
155(2)
5.2 Parzen-Window-Based Normalized Mutual Information,
157(6)
5.2.1 Definition of Parzen-Window Method,
157(1)
5.2.2 Parzen-Window-Based Estimation of Joint Histogram,
158(2)
5.2.3 Normalized Mutual Information and its Derivative,
160(3)
5.3 Analysis of Kernel Selection,
163(11)
5.3.1 The Designed Kernel,
163(4)
5.3.2 Comparison in Theory,
167(3)
5.3.3 Comparison by Experiments,
170(4)
5.4 Application on MR-Guided Microwave Thermocoagulation of Liver Tumors,
174(11)
5.4.1 Introduction of MR-Guided Microwave Thermocoagulation of Liver Tumors,
174(1)
5.4.2 Nonrigid Registration by Parzen-Window-Based Mutual Information,
175(2)
5.4.3 Evaluation on Phantom Data,
177(3)
5.4.4 Evaluation on Clinical Cases,
180(5)
5.5 Conclusion,
185(1)
Acknowledgements,
186(1)
References,
187(2)
6 2D/3D Image Registration For Endovascular Abdominal Aortic Aneurysm (AAA) Repair
189(40)
Shun Miao
Rui Liao
6.1 Introduction,
189(1)
6.2 Background,
190(9)
6.2.1 Image Modalities,
190(2)
6.2.2 2D/3D Registration Framework,
192(2)
6.2.3 Feature-Based Registration,
194(2)
6.2.4 Intensity-Based Registration,
196(1)
6.2.5 Number of Imaging Planes,
197(1)
6.2.6 2D/3D Registration for Endovascular AAA Repair,
198(1)
6.3 Smart Utilization of Two X-Ray Images for Rigid-Body 2D/3D Registration,
199(12)
6.3.1 2D/3D Registration: Challenges in EVAR,
199(3)
6.3.2 3D Image Processing and DRR Generation,
202(1)
6.3.3 2D Image Processing,
203(2)
6.3.4 Similarity Measure,
205(2)
6.3.5 Optimization,
207(3)
6.3.6 Validation,
210(1)
6.4 Deformable 2D/3D Registration,
211(9)
6.4.1 Problem Formulation,
212(1)
6.4.2 Graph-Based Difference Measure,
213(2)
6.4.3 Length Preserving Term,
215(1)
6.4.4 Smoothness Term,
215(1)
6.4.5 Optimization,
216(1)
6.4.6 Validation,
217(3)
6.5 Visual Check of Patient Movement Using Pelvis Boundary Detection,
220(2)
6.6 Discussion and Conclusion,
222(1)
References,
223(6)
Part IV Object Tracking 229(46)
7 Motion Tracking in Medical Images
231(44)
Chuqing Cao
Chao Li
Ying Sun
7.1 Introduction,
232(2)
7.1.1 Point-Based Tracking,
233(1)
7.1.2 Silhouette-Based Tracking,
233(1)
7.1.3 Kernel-Based Tracking,
233(1)
7.2 Background,
234(4)
7.2.1 Point-Based Tracking,
234(2)
7.2.2 Silhouette-Based Tracking,
236(1)
7.2.3 Kernel-Based Tracking,
237(1)
7.2.4 Summary,
238(1)
7.3 Bayesian Tracking Methods,
238(3)
7.3.1 Kalman Filters,
239(1)
7.3.2 Particle Filters,
240(1)
7.3.3 Summary of Bayesian Tracking Methods,
241(1)
7.4 Deformable Models,
241(5)
7.4.1 Mathematical Foundations of Deformable Models,
241(1)
7.4.2 Energy-Minimizing Deformable Models,
242(2)
7.4.3 Probabilistic Deformable Models,
244(1)
7.4.4 Summary of Deformable Models,
245(1)
7.5 Motion Tracking Based on the Harmonic Phase Algorithm,
246(4)
7.5.1 HARP Imaging,
246(2)
7.5.2 HARP Tracking,
248(1)
7.5.3 Summary,
249(1)
7.6 Case Study: Pseudo Ground Truth-Based Nonrigid Registration of MRI for Tracking the Cardiac Motion,
250(14)
7.6.1 Data Fidelity Term,
251(1)
7.6.2 Spatial Smoothness Constraint,
252(1)
7.6.3 Temporal Smoothness Constraint,
253(1)
7.6.4 Energy Minimization,
254(1)
7.6.5 Preliminary Results,
255(1)
7.6.6 Nonrigid Registration of Myocardial Perfusion MRI,
255(4)
7.6.7 Experimental Results,
259(5)
7.7 Discussion,
264(1)
7.8 Conclusion and Near-Future Trends,
265(2)
References,
267(8)
Part V Classification 275(118)
8 Blood Smear Analysis, Malaria Infection Detection, and Grading from Blood Cell Images
277(48)
Wei Xiong
Sim-Heng Ong
Joo-Hwee Lim
Jierong Cheng
Ying Gu
8.1 Introduction,
278(4)
8.2 Pattern Classification Techniques,
282(5)
8.2.1 Supervised and Nonsupervised Learning,
282(1)
8.2.2 Bayesian Decision Theory,
283(1)
8.2.3 Clustering,
284(2)
8.2.4 Support Vector Machines,
286(1)
8.3 GWA Detection,
287(8)
8.3.1 Image Analysis,
288(1)
8.3.2 Association between the Object Area and the Number of Cells Per Object,
289(2)
8.3.3 Clump Splitting,
291(2)
8.3.4 Clump Characterization,
293(2)
8.3.5 Classification,
295(1)
8.4 Dual-Model-Guided Image Segmentation and Recognition,
295(7)
8.4.1 Related Work,
296(1)
8.4.2 Strategies and Object Functions,
297(2)
8.4.3 Endpoint Adjacency Map Construction and Edge Linking,
299(1)
8.4.4 Parsing Contours and Their Convex Hulls,
300(1)
8.4.5 A Recursive and Greedy Splitting Approach,
301(1)
8.4.6 Incremental Model Updating and Bayesian Decision,
301(1)
8.5 Infection Detection and Staging,
302(3)
8.5.1 Related Work,
302(1)
8.5.2 Methodology,
303(2)
8.6 Experimental Results,
305(15)
8.6.1 GWA Classification,
305(5)
8.6.2 RBC Segmentation,
310(5)
8.6.3 RBC Classification,
315(5)
8.7 Summary,
320(1)
References,
321(4)
9 Liver Tumor Segmentation Using SVM Framework and Pathology Characterization Using Content-Based Image Retrieval
325(36)
Jiayin Zhou
Yanling Chi
Weimin Huang
Wei Xiong
Wenyu Chen
Jimin Liu
Sudhakar K. Venkatesh
9.1 Introduction,
325(2)
9.2 Liver Tumor Segmentation Under a Hybrid SVM Framework,
327(11)
9.2.1 Fundamentals of SVM for Classification,
327(3)
9.2.2 SVM Framework for Liver Tumor Segmentation and the Problems,
330(1)
9.2.3 A Three-Stage Hybrid SVM Scheme for Liver Tumor Segmentation,
331(3)
9.2.4 Experiment,
334(1)
9.2.5 Evaluation Metrics,
335(1)
9.2.6 Results,
336(2)
9.3 Liver Tumor Characterization by Content-Based Image Retrieval,
338(13)
9.3.1 Existing Work and the Rationale of Using CBIR,
339(1)
9.3.2 Methodology Overview and Preprocessing,
340(1)
9.3.3 Tumor Feature Representation,
341(2)
9.3.4 Similarity Query and Tumor Pathological Type Prediction,
343(2)
9.3.5 Experiment,
345(1)
9.3.6 Results,
346(5)
9.4 Discussion,
351(5)
9.4.1 About Liver Tumor Segmentation Using Machine Learning,
351(2)
9.4.2 About Liver Tumor Characterization Using CBIR,
353(3)
9.5 Conclusion,
356(1)
References,
357(4)
10 Benchmarking Lymph Node Metastasis Classification for Gastric Cancer Staging
361(32)
Su Zhang
Chao Li
Shuheng Zhang
Lifang Pang
Huan Zhang
10.1 Introduction,
362(5)
10.1.1 Introduction of GSI-CT,
363(3)
10.1.2 Imaging Findings of Gastric Cancer,
366(1)
10.2 Related Feature Selection, Metric Learning, and Classification Methods,
367(10)
10.2.1 Feature Extraction,
367(1)
10.2.2 KNN,
367(2)
10.2.3 Feature Selection,
369(5)
10.2.4 AdaBoost and EAdaBoost Algorithms,
374(3)
10.3 Preprocessing Method for GSI-CT Data,
377(2)
10.3.1 Data Acquisition for GSI-CT Data,
377(1)
10.3.2 Univariate Analysis,
378(1)
10.4 Classification Results For GSI-CT Data of Gastric Cancer,
379(9)
10.4.1 Experimental Results of mRMR-KNN,
379(4)
10.4.2 Experimental Results of SFS-KNN,
383(2)
10.4.3 Experimental Results of Metric Learning,
385(1)
10.4.4 Experiments Results of AdaBoost and EAdaBoost,
385(3)
10.4.5 Experiment Analysis,
388(1)
10.5 Conclusion and Future Work,
388(1)
Acknowledgment,
388(1)
References,
388(5)
Part VI Knowledge-Based Systems 393(70)
11 The Use of Knowledge in Biomedical Image Analysis
395(34)
Florence Cloppet
11.1 Introduction,
395(2)
11.2 Data, Information, and Knowledge?
397(2)
11.2.1 Data Versus Information,
397(1)
11.2.2 Knowledge Versus Information,
398(1)
11.3 What Kind of Information/Knowledge Can be Introduced?
399(1)
11.4 How to Introduce Information in Computer Vision Systems?
400(18)
11.4.1 Nature of Prior Information/Knowledge,
402(6)
11.4.2 Frameworks Allowing Prior Information Introduction,
408(10)
11.5 Conclusion,
418(1)
References,
418(11)
12 Active Shape Model for Contour Detection of Anatomical Structure
429(34)
Magi Li
Qing Nie
12.1 Introduction,
429(1)
12.2 Background,
430(4)
12.2.1 Free-Form Deformable Models,
430(2)
12.2.2 Parametrically Deformable Models,
432(2)
12.3 Methodology,
434(6)
12.3.1 Point Distribution Model,
434(2)
12.3.2 Active Shape Model (ASM),
436(2)
12.3.3 A Modified ASM,
438(2)
12.4 Applications,
440(16)
12.4.1 Boundary Detection of Optic Disk,
440(10)
12.4.2 Lens Structure Detection,
450(6)
12.5 Summary,
456(1)
Acknowledgment,
457(1)
References,
457(6)
Index 463
Joo-Hwee Lim is the Head of the Visual Computing Department at the Institute for Infocomm Research (I2R), A*STAR, Singapore, and an Adjunct Associate Professor at the School of Computer Engineering, Nanyang Technological University, Singapore. He is the co-Director of IPAL (Image & Pervasive Access Laboratory), a French-Singapore Joint Lab. He established the medical image analysis group at I2R in 2006, collaborating with clinicians closely, resulting in strong competency in ocular imaging, brain image analysis, cell image understanding etc at the institute. He has published over 200 journal and conference papers and owns 17 patents in the areas of computer vision, cognitive vision, pattern recognition, and medical image analysis.

Sim-Heng Ong is an Associate Professor in the Departments of Electrical Engineering and Bioengineering at the National University of Singapore. He received his PhD from the University of Sydney, Australia. His major research areas are computer vision and medical image analysis and visualization. He has worked extensively with clinicians in developing algorithms for a variety of medical applications, and has publications in many highly respected journals and conferences. Wei Xiong is a Research Scientist at the Institute for Infocomm Research (I2R), A*STAR, Singapore. He obtained his PhD degree from the National University of Singapore.  His research interest is in computer vision, image processing, pattern classification and acoustic imaging. Dr. Xiong has published over 60 technical papers.
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