Image Registration for Biomedical Information Integration

Image Registration for Biomedical Information Integration

Xiu Ying Wang (BMIT Research Group at The University of Sydney, Australia) and Dagan Feng (BMIT Research Group, The University of Sydney, Australia)
DOI: 10.4018/978-1-60566-218-3.ch006
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Abstract

The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new challenges in medical knowledge discovery from multi-imaging modalities and management. In this chapter, biomedical image registration and fusion, which is an effective mechanism to assist medical knowledge discovery by integrating and simultaneously representing relevant information from diverse imaging resources, is introduced. This chapter covers fundamental knowledge and major methodologies of biomedical image registration, and major applications of image registration in biomedicine. Further, discussions on research perspectives are presented to inspire novel registration ideas for general clinical practice to improve the quality and efficiency of healthcare.
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Introduction

With the rapid advance in digital imaging techniques and reduction of cost in data acquisition, widely available biomedical datasets acquired from diverse medical imaging modalities and collected over different imaging sessions are becoming essential information resources for high-quality healthcare services. Anatomical imaging modalities such as Magnetic Resonance (MR) imaging, Computed Tomography (CT) and X-ray mainly provide detailed morphological structures. Functional imaging modalities such as Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) primarily reveal information about the underlying biochemical and physiological changes. More recently, the combination of functional and anatomical imaging technologies into a single device, PET/CT and SPECT/CT scanners, has widened the array of biomedical imaging approaches and offered new challenges in efficient and intelligent use of imaging data. Since each of these imaging technologies can have their own inherent value for patient management, and ideally all such imaging data would be accessible for the one individual when they are required, huge volumes of biomedical imaging datasets are generated daily in the clinical practice (Wang et al, 2007). However, these ever-increasing huge amounts of datasets unavoidably cause information repositories to overload and pose substantial challenges in effective and efficient medical knowledge management, imaging data retrieval, and patient management.

Biomedical image registration is an effective mechanism for maximizing the complementary and relevant information embedded in various image datasets. By establishing spatial correspondence among the multiple datasets, biomedical image registration enables seamless integration and full utilization of heterogeneous image information, thereby providing a more complete insight into medical data (Wang, Feng, 2005) to facilitate knowledge discovery and management of patients with a variety of diseases.

Biomedical image registration has important applications in medical database management, for instance, patient record management, medical image retrieval and compression. Image registration is essential in constructing statistical atlases and templates to capture and encode morphological or functional patterns across a large specific population (Wang and Feng, 2005) . The automatic registration between patient datasets and these available templates can be used in the automatic segmentation and interpolation of structures and tissues, and the detection of pathologies. Registration and fusion of information from multiple, diverse imaging resources is critical for accurate clinical decision making, treatment planning and assessment, detecting and monitoring dynamic changes in structures and functions, and is important to minimally invasive treatment (Wang, Feng, 2005).

Due to its research significance and crucial role in clinical applications, biomedical image registration has been extensively studied during last three decades (Brown, 1992; Maintz et al., 1998; Fitzpatrick et al. 2000). The existing registration methodologies can be catalogued into different categories according to criteria such as image dimensionality, registration feature space, image modality, and subjects involved (Brown, 1992). Different Region-of-Interests (ROIs) and various application requirements and scenarios are key reasons for continuously introducing new registration algorithms. In addition to a large number of software-based registration algorithms, more advanced imaging devices such as combined PET/CT and SPECT/CT scanners provide hardware-based solutions for the registration and fusion by performing the functional and anatomical imaging in the one imaging session with the one device. However, it remains challenging to generate clinically applicable registration with improved performance and accelerated computation for biomedical datasets with larger imaging ranges, higher resolutions, and more dimensionalities.

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Table of Contents
Foreword
Riccardo Bellazzi
Preface
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Acknowledgment
Petr Berka, Jan Rauch, Djamel Abdelkader Zighed
Chapter 1
Jana Zvárová, Arnošt Veselý
This chapter introduces the basic concepts of medical informatics: data, information, and knowledge. Data are classified into various types and... Sample PDF
Data, Information and Knowledge
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Chapter 2
Michel Simonet, Radja Messai, Gayo Diallo
Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status... Sample PDF
Ontologies in the Health Field
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Chapter 3
Alberto Freitas, Pavel Brazdil, Altamiro Costa-Pereira
This chapter introduces cost-sensitive learning and its importance in medicine. Health managers and clinicians often need models that try to... Sample PDF
Cost-Sensitive Learning in Medicine
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Chapter 4
Arnošt Veselý
This chapter deals with applications of artificial neural networks in classification and regression problems. Based on theoretical analysis it... Sample PDF
Classification and Prediction with Neural Networks
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Chapter 5
Patrik Eklund, Lena Kallin Westin
Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data... Sample PDF
Preprocessing Perceptrons and Multivariate Decision Limits
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Chapter 6
Xiu Ying Wang, Dagan Feng
The rapid advance and innovation in medical imaging techniques offer significant improvement in healthcare services, as well as provide new... Sample PDF
Image Registration for Biomedical Information Integration
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Chapter 7
ECG Processing  (pages 137-160)
Lenka Lhotská, Václav Chudácek, Michal Huptych
This chapter describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG)... Sample PDF
ECG Processing
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Chapter 8
EEG Data Mining Using PCA  (pages 161-180)
Lenka Lhotská, Vladimír Krajca, Jitka Mohylová, Svojmil Petránek, Václav Gerla
This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing.... Sample PDF
EEG Data Mining Using PCA
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Chapter 9
Darryl N. Davis, Thuy T.T. Nguyen
Risk prediction models are of great interest to clinicians. They offer an explicit and repeatable means to aide the selection, from a general... Sample PDF
Generating and Verifying Risk Prediction Models using Data Mining
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Chapter 10
Vangelis Karkaletsis, Konstantinos Stamatakis, Karampiperis, Karampiperis, Pythagoras Karampiperis, Pythagoras Karampiperis
The World Wide Web is an important channel of information exchange in many domains, including the medical one. The ever increasing amount of freely... Sample PDF
Management of Medical Website Quality Labels via Web Mining
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Chapter 11
Rainer Schmidt
In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal... Sample PDF
Two Case-Based Systems for Explaining Exceptions in Medicine
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Chapter 12
Bruno Crémilleux, Arnaud Soulet, Jiri Kléma, Céline Hébert, Olivier Gandrillon
The discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on... Sample PDF
Discovering Knowledge from Local Patterns in SAGE Data
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Chapter 13
Jirí Kléma, Filip Železný, Igor Trajkovski, Filip Karel, Bruno Crémilleux
This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several... Sample PDF
Gene Expression Mining Guided by Background Knowledge
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Chapter 14
Pamela L. Thompson, Xin Zhang, Wenxin Jiang, Zbigniew W. Ras, Pawel Jastreboff
This chapter describes the process used to mine a database containing data, related to patient visits during Tinnitus Retraining Therapy. The... Sample PDF
Mining Tinnitus Database for Knowledge
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Chapter 15
Dinora A. Morales, Endika Bengoetxea, Pedro Larrañaga
Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to... Sample PDF
Gaussian-Stacking Multiclassifiers for Human Embryo Selection
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Chapter 16
Mining Tuberculosis Data  (pages 332-349)
Marisa A. Sánchez, Sonia Uremovich, Pablo Acrogliano
This chapter reviews the current policies of tuberculosis control programs for the diagnosis of tuberculosis. The international standard for... Sample PDF
Mining Tuberculosis Data
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Chapter 17
Mila Kwiatkowska, M. Stella Atkins, Les Matthews, Najib T. Ayas, C. Frank Ryan
This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules.... Sample PDF
Knowledge-Based Induction of Clinical Prediction Rules
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Chapter 18
Petr Berka, Jan Rauch, Marie Tomecková
The aim of this chapter is to describe goals, current results, and further plans of long-time activity concerning application of data mining and... Sample PDF
Data Mining in Atherosclerosis Risk Factor Data
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About the Contributors