Table of Contents

Cover

Title Page

Series

Copyright

List of Contributors

Preface

Acronyms

Part I: Introduction

Chapter 1: Overview of Biomedical Image Understanding Methods

1.1 Segmentation and Object Detection

1.2 Registration

1.3 Object Tracking

1.4 Classification

1.5 Knowledge-Based Systems

References

Part II: Segmentation and Object Detection

Chapter 2: Medical Image Segmentation and Its Application in Cardiac MRI

2.1 Introduction

2.2 Background

2.3 Parametric Active Contours—The Snakes

2.4 Geometric Active Contours—The Level Sets

2.5 Graph-Based Methods—The Graph Cuts

2.6 Case Study: Cardiac Image Segmentation Using a Dual Level Sets Model

2.7 Conclusion and Near-Future Trends

References

Chapter 3: Morphometric Measurements of The Retinal Vasculature in Fundus Images with Vampire

3.1 Introduction

3.2 Assessing Vessel Width

3.3 Artery or Vein?

3.4 Are My Program'S Measurements Accurate?

References

Chapter 4: Analyzing Cell and Tissue Morphologies Using Pattern Recognition Algorithms

4.1 Introduction

4.2 Texture Segmentation of Endometrial Images Using the Subspace Mumford–Shah Model

4.3 Spot Clustering for Detection of Mutantsin Keratinocytes

4.4 Cells and Nuclei Detection

4.5 Geometric Regional Graph Spectral Feature

4.6 Mitotic Cells in the H&E Histopathological Images of Breast Cancer Carcinoma

4.7 Conclusions

References

Part III: Registration and Matching

Chapter 5: 3D Nonrigid Image Registration by Parzen-Window-Based Normalized Mutual Information and its Application on Mr-Guided Microwave Thermocoagulation of Liver Tumors

5.1 Introduction

5.2 Parzen-Window-Based Normalized Mutual Information

5.3 Analysis of Kernel Selection

5.4 Application on MR-Guided Microwave Thermocoagulation of Liver Tumors

5.5 Conclusion

Acknowledgements

References

Chapter 6: 2D/3D Image Registration for Endovascular Abdominal Aortic Aneurysm (AAA) Repair

6.1 Introduction*

6.2 Background

6.3 Smart Utilization of two X-ray Images for Rigid-Body 2D/3D Registration

6.4 Deformable 2D/3D Registration

6.5 Visual Check of Patient Movement Using Pelvis Boundary Detection

6.6 Discussion and Conclusion

References

Part IV: Object Tracking

Chapter 7: Motion Tracking in Medical Images

7.1 Introduction

7.2 Background

7.3 Bayesian Tracking Methods

7.4 Deformable Models

7.5 Motion Tracking Based on The Harmonic Phase Algorithm

7.6 Case Study: Pseudo Ground Truth-Based Nonrigid Registration of Mri for Tracking The Cardiac Motion

7.7 Discussion

7.8 Conclusion and Near-Future Trends

References

Part V: Classification

Chapter 8: Blood Smear Analysis, Malaria Infection Detection, And Grading From Blood Cell Images

8.1 Introduction*

8.2 Pattern Classification Techniques

8.3 Gwa Detection

8.4 Dual-Model-Guided Image Segmentation and Recognition

8.5 Infection Detection and Staging

8.6 Experimental Results

8.7 Summary

References

Chapter 9: Liver Tumor Segmentation Using SVM Framework and Pathology Characterization Using Content-Based Image Retrieval

9.1 Introduction*

9.2 Liver Tumor Segmentation Under a Hybrid SVM Framework

9.3 Liver Tumor Characterization by Content-Based Image Retrieval

9.4 Discussion

9.5 Conclusion

References

Chapter 10: Benchmarking Lymph Node Metastasis ClassificationFor Gastric Cancer Staging

10.1 Introduction*

10.2 Related Feature Selection, Metric Learning,and Classification Methods

10.3 Preprocessing Method for GSI-CT Data

10.4 Classification Results For GSI-CT Data of Gastric Cancer

10.5 Conclusion and Future Work

Acknowledgment

References

Part VI: Knowledge-Based Systems

Chapter 11: The use of Knowledge in Biomedical Image Analysis

11.1 Introduction

11.2 Data, Information, and Knowledge?

11.3 What Kind of Information/Knowledge Can be Introduced?

11.4 How to Introduce Information in Computer Vision Systems?

11.5 Conclusion

References

Chapter 12: Active Shape Model for Contour Detection of Anatomical Structure

12.1 Introduction

12.2 Background

12.3 Methodology

12.4 Applications

12.5 Summary

Acknowledgment

References

Index

End User License Agreement

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Guide

Cover

Table of Contents

Preface

Part I: Introduction

Chapter 1: Overview of Biomedical Image Understanding Methods

List of Illustrations

Figure 1.1

Figure 1.2

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 2.5

Figure 2.6

Figure 2.7

Figure 2.8

Figure 2.9

Figure 2.10

Figure 2.11

Figure 2.12

Figure 3.1

Figure 3.3

Figure 3.2

Figure 3.4

Figure 4.1

Figure 4.2

Figure 4.3

Figure 4.4

Figure 4.5

Figure 4.6

Figure 4.7

Figure 4.16

Figure 4.10

Figure 4.11

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Figure 4.9

Figure 4.12

Figure 4.13

Figure 4.14

Figure 4.15

Figure 4.17

Figure 4.18

Figure 4.19

Figure 4.20

Figure 4.21

Figure 5.1

Figure 5.2

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.7

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Figure 5.12

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Figure 6.1

Figure 6.2

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Figure 6.10

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Figure 7.2

Figure 7.3

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Figure 7.10

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Figure 9.3

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Figure 9.17

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Figure 10.1

Figure 10.2

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List of Tables

Table 1.1

Table 1.3

Table 1.4

Table 1.5

Table 2.1

Table 3.1

Table 3.2

Table 3.3

Table 5.1

Table 5.2

Table 6.1

Table 6.2

Table 6.3

Table 6.4

Table 6.5

Table 6.6

Table 7.1

Table 8.1

Table 8.2

Table 8.3

Table 8.4

Table 8.5

Table 8.6

Table 8.7

Table 8.8

Table 9.1

Table 10.1

Table 10.2

Table 10.3

Table 10.4

Table 10.5

Table 10.6

Table 10.7

Table 10.8

Table 10.9

Table 10.10

Table 12.1

Table 12.2

Table 12.3

Biomedical Image Understanding

Methods and Applications

Joo-Hwee LIM

Institute for Infocomm Research

Sim-Heng ONG

National University of Singapore

Wei XIONG

Institute for Infocomm Research

Copyright © 2015 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

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Library of Congress Cataloging-in-Publication Data is available:

Biomedical image understanding : methods and applications / [edited by] Joo Hwee Lim, Sim Heng Ong, Wei Xiong.

p. ; cm. – (Wiley Series in biomedical engineering and multidisciplinary integrated systems)

Includes bibliographical references and index.

ISBN 978-1-118-71515-4 (cloth)

I. Lim, Joo Hwee, 1964- editor. II. Ong, Sim Heng, 1955- editor. III. Xiong, Wei, 1966- editor.

[DNLM: 1. Image Interpretation, Computer-Assisted. 2. Image Enhancement–methods. 3. Image Processing, Computer-Assisted. 4. Pattern Recognition, Automated–methods. WB 141]

R857.O6

610.28′4–dc23

2014016560

List of Contributors

Lucia Ballerini VAMPIRE/CVIP, School of Computing, University of Dundee, Dundee, United Kingdom

Chuqing Cao Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Alessandro Cavinato VAMPIRE/CVIP, School of Computing, University of Dundee, Dundee, United Kingdom

Wenyu Chen Institute for Infocomm Research, A*STAR, Singapore

Yen-Wei Chen College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan

Jierong Cheng Institute for Infocomm Research, A*STAR, Singapore

Yanling Chi Singapore Bio-Imaging Consortium, A*STAR, Singapore

Florence Cloppet Lipade, Université Paris Descartes, Paris, France

Andrea Giachetti Department of Computer Science, University of Verona, Verona, Italy

Ying Gu Institute for Infocomm Research, A*STAR, Singapore

Chao-Hui Huang Bioinformatics Institute, A*STAR, Singapore

Weimin Huang Institute for Infocomm Research, A*STAR, Singapore

Yoshimasa Kurumi Department of Surgery, Shiga University of Medical Science, Shiga, Japan

Yan Nei Law Bioinformatics Institute, A*STAR, Singapore

Hwee Kuan Lee Bioinformatics Institute, A*STAR, Singapore

Chao Li Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Chao Li School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Huiqi Li School of Information and Electronics, Beijing Institute of Technology, China

Shimiao Li Institute for Infocomm Research, A*STAR, Singapore

Rui Liao Siemens Corporation, Corporate Technology, Princeton, New Jersey, United States

Joo Hwee Lim Institute for Infocomm Research, A*STAR, Singapore

Jimin Liu Singapore Bio-Imaging Consortium, A*STAR, Singapore

Tom MacGillivray Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom

Shun Miao Siemens Corporation, Corporate Technology, Princeton, New Jersey, United States

Shigehiro Morikawa Department of Fundamental Nursing, Shiga University of Medical Science, Shiga, Japan

Qing Nie School of Information and Electronics, Beijing Institute of Technology, China

Sim-Heng Ong Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Lifang Pang Department of Radiology, Shanghai Ruijin Hospital, Shanghai, China

Devan-Jali Relan Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom

Ying Sun Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Emanuele Trucco VAMPIRE/CVIP, School of Computing, University of Dundee, Dundee, United Kingdom

Sudhakar K. Venkatesh Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States

Dong Wei Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Wei Xiong Institute for Infocomm Research, A*STAR, Singapore

Rui Xu Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, Shiga, Japan

Choon Kong Yap Bioinformatics Institute, A*STAR, Singapore

Huan Zhang Department of Radiology, Shanghai Ruijin Hospital, Shanghai, China

Shuheng Zhang School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Su Zhang School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

Jiayin Zhou Institute for Infocomm Research, A*STAR, Singapore

Preface

Modern imaging devices generate enormous amounts of digital images in biological, medical, pathological, and functional applications. Computerized image analysis plays an important role in understanding and interpreting these images accurately and efficiently to assist biologists and clinicians in decision making. Being a highly multidisciplinary research field, biomedical image understanding requires knowledge, theories, methods, and techniques from computer science, engineering, mathematics as well as from biology, medicine, pathology, dentistry, and other specialized healthcare domains. Over the past decade, developments in related disciplines have rapidly advanced research and applications in biomedical image understanding.

This book is intended for researchers, teachers, and graduate students in this exciting and fast-changing field. In particular, it brings together experts to provide representative accounts of the concepts and applications in biomedical image understanding involving different image modalities of relevant anatomies for the purpose of pathology identification and assessment. The book focuses on image understanding and semantic interpretation with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications.

The book consists of 12 chapters. Each chapter focuses on a biomedical imaging topic and is self-contained with an introduction to basic concepts, principles, methods, and a case study or application.

Part I, Introduction, contains one chapter, which is an overview of biomedical image understanding methods by Xiong et al. This chapter provides an extensive review of the taxonomy, state-of-art, and clinical applications of five important areas in biomedical image understanding: segmentation and object detection, registration and matching, object tracking, classification, and knowledge-based systems.

Part II, Segmentation and Object Detection, comprises three chapters.

Chapter 2, by Wei et al., introduces three classes of segmentation techniques widely used in medical images, namely, parametric active contours, geometric active contours, and graph-based techniques, followed by a detailed presentation of a representative example of each technique. These are, respectively, the snake, level set, and graph cut. A case study on short-axis cardiac image segmentation is used to illustrate some of the principles covered in the chapter.

A number of systemic diseases may lead to distinct changes in retinal blood vessels, which are easily observed by fundus photography. Chapter 3, by Trucco et al.focuses on three important topics in the morphometric measurement of retinal vasculature: vessel width estimation, artery–vein classification, and validation. Key concepts and challenges are presented followed by the authors' solutions and validation of results.

Chapter 4, by Lee et al., introduces readers to the diversity of bioimage informatics by presenting a variety of applications: the detection of objects using image processing approaches for diagnosis of premalignant endometrial disease, mutant detection in microscopy images of in vitro cultured keratinocytes, and cell and nuclei detection in microscopy images. Contrary to the image processing-based algorithms, the authors describe pattern recognition-based approaches and illustrate how they are used for detecting phenotypic changes in keratin proteins and mitotic cells in breast cancer histological images.

Part III, Registration and Matching, comprises two chapters.

Registration across different modalities is an important theme in medical image processing. In Chapter 5, Xu et al. describe the application of the Parzen-window-based method to normalized mutual information for 3D nonrigid registration. Attention is paid to the choice of kernel, which is critical to achieve good performance. The authors describe a demonstration of this approach in the computer tomography-magnetic resonance (CT-MR) nonrigid registration of liver images for MR-guided microwave thermocoagulation of liver tumors.

In the second chapter on registration, Miao and Liao describe a 2D/3D registration system for aligning a preoperative CT scan with intraoperative 2D X-ray projections of the same patient. The algorithm was specifically designed to provide accurate visualization of arterial anatomy for guidance and navigation to the surgeon in endovascular aneurysm repair. The approach can potentially be applied to a wide set of interventional procedures in orthopedics, cardiology, and neurology.

Part IV, Object Tracking, comprises one chapter. In Chapter 7, Cao et al. describe three categories of tracking techniques that are popularly used in medical image analysis: point tracking, silhouette tracking, and kernel tracking. A most representative method for each of the three general techniques is introduced in detail, namely, Bayesian tracking methods, deformable models, and harmonic phase (HARP) algorithm. A case study on cardiac motion tracking in myocardial perfusion magnetic resonance imaging (MRI) is also presented.

Part V, Classification, comprises three chapters.

In Chapter 8, Xiong et al. introduce the pattern classification techniques widely used in biomedical image understanding. A case study on a framework of blood cell image analysis is presented; the major components include good working area detection from the entire blood smear region, segmentation and recognition of blood cell images, and malaria infection detection and staging.

Liver tumor volume is an important measure of the severity of the disease. Moreover, correct pathological characterization of the tumor is crucial in deciding on the treatment options. In Chapter 9, Zhou et al. present a semiautomated method forthe segmentation of liver tumors from CT scans under a hybrid support vector machine (SVM) framework and a content-based image retrieval prototype system based on multiphase CT images to support the decision making for liver tumor characterization.

Chapter 10 by Zhang et al. reports on the application of machine learning methods on computerized GSI-CT data analysis of lymph node metastasis in gastric cancer. The pipeline consists of region of interest (ROI) segmentation, feature extraction, feature selection, metric learning, and classification. Finally, the performance of the different classification models based on k-nearest neighbor is analyzed.

Part VI, Knowledge-based Systems, comprises two chapters.

Chapter 11, by Cloppet, explains the type of information or knowledge that can be introduced in image processing and the different ways by which they can be integrated into cutting-edge methods for biomedical image analysis. The appropriate use of information or knowledge can help to make image analysis methods more robust to variability and to interpret biomedical images in a more appropriate manner.

The active shape model (ASM) is a parametric deformable model popular for object modeling and representation. The original ASM and a modified model are presented in Chapter 12 by Li et al. Two applications of ASM in boundary detection of anatomical structures are investigated: boundary detection of optic disk in fundus images and lens structure detection in lens images.

We would like to take this opportunity to acknowledge the people who motivated and contributed to the book in one way or the other. In October 2011, Professor Kai Chang from Texas A&M University invited us to contribute a book to the series of “Biomedical Engineering and Multi-Disciplinary Integrated Systems” published by Wiley-Interscience. This motivated us to work together to plan for this book.

We would like to thank the people who had offered a great deal of help during the editing of this book. The 12 chapters are contributed by 39 authors/coauthors, whose names and affiliations are listed in the “Contributors” section. Anonymous reviewers provided constructive suggestions during the book planning in two cycles of reviewing. Each chapter was reviewed by at least two experts who provided critical comments. The authors have responded to the review comments with rebuttals and necessary changes. We are grateful to the reviewers for their hardwork: Dr. B. T. Thomas Yeo, Dr. C. K. Chui, Dr. B. N. Li, J. Cheng, Dr. Y. Wang, Dr. W. Huang, Dr. J. Zhou, Dr. L. Li, Dr. S. Lu, Dr. S. Li, Dr. J. Cheng, and Dr. Y. Gu. We also wish to thank Mr. S. C. Chia who contributed figure drawings and Dr. W. Chen who participated in technical discussions.

We have compiled in one volume a broad overview of the exciting area of biomedical image understanding. Significant progress has been made in this field in recent years, and we hope that readers will obtain a flavor of the exciting work that is being done, and at the same time enjoy reading this book as much as we have enjoyed the process of compiling it.

Acronyms

AAA | abdominal aortic aneurysm
AAM | active appearance model
AI | artificial intelligence
ASM | active shape model
BN | Bayesian networks
BSVC | binary support vector classifier
CBIR | content-based image retrieval
CFS | correlation feature selection
CRFs | conditional random fields
CT | computed tomography
DRR | digitally reconstructed radiography
DSA | digitally subtracted angiography
EM | expectation-maximization
EVAR | endovascular aneurysm repair
FFD | free form deformation
GD | gradient difference
GMM | Gaussian mixture model
GSI | Gemstone spectral imaging
GVF | gradient vector flow
GWA | good working areas
HARP | harmonic phase
HCC | hepatocellular carcinoma
H&E | hematein and eosin
HU | Hounsfield unit
ICA | independent component analysis
KNN | k-nearest neighbor
LDC | linear discriminant classifier
LV | left ventricle
MAP | maximum a posteriori
MDCT | multidetector-row CT
MDS | multidimensional scaling
MI | mutual information
MRF | Markov random field
MRI | magnetic resonance imaging
mRMR | minimum-redundancy-maximum-relevance
NMI | normalized mutual information
OSVC | one-class support vector classifier
PCA | principal component analysis
PDE | partial differential equations
PDF | probability density function
PDM | point distribution model
PET | positron emission tomography
PGT | pseudoground truth
RBC | red blood cell
RBF | radius basis function
RIA | retinal image analysis
RMS | root mean square
ROI | region of interest
RV | right ventricle
SBS | sequential backward selection
SFS | sequential forward selection
SMS | subspace Mumford–Shah model
SPAMM | spatially modulated magnetization
SSM | statistical shape model
SVC | support vector classifier
SVM | support vector machine
TPS | thin plate spline
VPS | volume percentage stroma
WSI | whole slide images

Part I

Introduction

1
Overview of Biomedical Image Understanding Methods

Wei Xiong, Jierong Cheng, Ying Gu, Shimiao Li and Joo-Hwee Lim

Department of Visual Computing, Institute for Infocomm Research, A*STAR, Singapore

Computerized image understanding is the process of extracting meaningful features (e.g., color, intensity, and geometry of group of pixels) from the images, inferring and aggregating the symbolic information into unique concepts, matching them with physical world models and producing descriptions of the images and their relationship in the world that the images represent [1]. Biomedical images are those acquired from biology, medicine, pathology, dentistry, and other specialized healthcare domains. With the advancement of modern imaging devices, enormous amounts of digital still and dynamic image data are generated from nano to macro, from protein to cells, and to organs and from animals to human. Computerized image analysis plays an important role in understanding and interpreting these images accurately and efficiently to assist biologists and clinicians in decision making. Being a highly multidisciplinary research field, biomedical image understanding requires knowledge, theories, methods, and techniques from computer science, engineering, mathematics as well as from general and specialized healthcare domains. Developments in related disciplines have rapidly advanced over the past decade. Various imaging modalities and acquiring procedures result in large differences in biomedical images.

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Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!