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I

Biomedical Imaging

Biomedical Imaging

Edited by

Youxin Mao

In-Tech

intechweb.org

Published by In-Teh

In-Teh

Olajnica 19/2, 32000 Vukovar, Croatia

Abstracting and non-profit use of the material is permitted with credit to the source. Statements and

opinions expressed in the chapters are these of the individual contributors and not necessarily those of

the editors or publisher. No responsibility is accepted for the accuracy of information contained in the

published articles. Publisher assumes no responsibility liability for any damage or injury to persons or

property arising out of the use of any materials, instructions, methods or ideas contained inside. After

this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any

publication of which they are an author or editor, and the make other personal use of the work.

© 2010 In-teh

www.intechweb.org

Additional copies can be obtained from:

publication@intechweb.org

First published March 2010

Printed in India

Technical Editor: Melita Horvat

Cover designed by Dino Smrekar

Biomedical Imaging,

Edited by Youxin Mao

p. cm.

ISBN 978-953-307-071-1

V

Preface

Biomedical imaging is becoming an indispensable branch within bioengineering. This research

field has recent expanded due to the requirement of high-level medical diagnostics and rapid

development of interdisciplinary modern technologies. This book is designed to present the

most recent advances in instrumentation, methods, and image processing as well as clinical

applications in important areas of biomedical imaging. This book provides broad coverage of

the field of biomedical imaging, with particular attention to an engineering viewpoint.

Chapter one introduces a 3D volumetric image registration technique. The foundations of

the volumetric image visualization, classification and registration are discussed in detail.

Although this highly accurate registration technique is established from three phantom

experiments (CT, MRI and PET/CT), it applies to all imaging modalities. Optical imaging has

recently experienced explosive growth due to the high resolution, noninvasive or minimally

invasive nature and cost-effectiveness of optical coherence modalities in medical diagnostics

and therapy. Chapter two demonstrates a fiber catheter-based complex swept-source optical

coherence tomography system. Swept-source, quadrature interferometer, and fiber probes

used in optical coherence tomography system are described in details. The results indicate that

optical coherence tomography is a potential imaging tool for in vivo and real-time diagnosis,

visualization and treatment monitoring in clinic environments. Brain computer interfaces have

attracted great interest in the last decade. Chapter three introduces brain imaging and machine

learning for brain computer interface. Non-invasive approaches for brain computer interface

are the main focus. Several techniques have been proposed to measure relevant features from

EEG or MRI signals and to decode the brain targets from those features. Such techniques

are reviewed in the chapter with a focus on a specific approach. The basic idea is to make

the comparison between a BCI system and the use of brain imaging in medical applications.

Texture analysis methods are useful for discriminating and studying both distinct and subtle

textures in multi-modality medical images. In chapter four, texture analysis is presented as

a useful computational method for discriminating between pathologically different regions

on medical images. This is particularly important given that biomedical image data with near

isotropic resolution is becoming more common in clinical environments.

VI

The goal of this book is to provide a wide-ranging forum in the biomedical imaging field

that integrates interdisciplinary research and development of interest to scientists, engineers,

teachers, students, and clinical providers. This book is suitable as both a professional reference

and as a text for a one-semester course for biomedical engineers or medical technology

students.

Youxin Mao

Institute for Microstructural Science,

National Research Council Canada

VII

Contents

Preface

V

1. Volumetric Image Registration of Multi-modality Images of CT, MRI and PET

001

Guang Li and Robert W. Miller

2. Full Range Swept-Source Optical Coherence Tomography with Ultra Small

Fiber Probes for Biomedical Imaging

027

Youxin Mao, Costel Flueraru and Shoude Chang

3. Brain Imaging and Machine Learning for Brain-Computer Interface

057

Maha Khachab, Chafic Mokbel, Salim Kaakour, Nicolas Saliba and Gérard Chollet

4. Texture Analysis Methods for Medical Image Characterisation

075

William Henry Nailon

VIII

Volumetric Image Registration of Multi-modality Images of CT, MRI and PET

1

Volumetric Image Registration of Multi-modality Images of CT, MRI and

1

PET

X

Guang Li and Robert W. Miller

Volumetric Image Registration of

Multi-modality Images of CT, MRI and PET

Guang Li and Robert W. Miller

National Cancer Institute, National Institutes of Health

Bethesda, Maryland,USA

1. Introduction

1.1 Biomedical Imaging of Multimodality

Three-dimensional (3D) biomedical imaging starts from computed tomography (CT) in

1960’s-1970’s (Cormack, 1963, Hounsfield, 1973) followed by magnetic resonance imaging

(MRI) in 1970’s (Lauterbur, 1973, Garroway et al, 1974, Mansfield & Maudsley, 1977). These

anatomical imaging techniques are based on physical features of a patient’s anatomy, such

as linear attenuation coefficient or electromagnetic interaction and relaxation. 3D biological

imaging (molecular imaging or functional imaging), such as positron emission tomography

(PET) and single photon emission computed tomography (SPECT), was also developed in

mid 1970’s (Ter-Pogossian, et al, 1975, Phelps, et al, 1975). They detect biological features

using a molecular probe, labelled with either a positron emitter or a gamma emitter, to

target a molecular, cellular or physiological event, process or product. So, the x-ray/γ-ray

intensity from a particular anatomical site is directly related to the concentration of the

radio-labelled molecular marker. Therefore, a biological event will be imaged in 3D space.

Since the concept of hybrid PET/CT scanner was introduced (Beyer, et al, 2000), the co-

registration of biological image with anatomical image offers both biological and anatomical

information in space, assuming that there is no patient’s motion between and during the

two image acquisitions. Other combined scanners, such as SPECT/CT and PET/MRI, have

also been developed (Cho, et al, 2007, Bybel, et al, 2008, Chowdhury & Scarsbrook, 2008).

Registration of biological and anatomical images at acquisition or post acquisition provides

multi-dimensional information on patient’s disease stage (Ling, et al, 2000), facilitating

lesion identification for diagnosis and target delineation for treatment.

In radiological clinic, although a particular imaging modality may be preferable to diagnose

a particular disease, multimodality imaging has been increasingly employed for early

diagnosing malignant lesion (Osman, et al, 2003), coronary artery diseases (Elhendy, et al

2002), and other diseases. Use of biological imaging enhances the success rate of correct

diagnosis, which is necessary for early, effective treatment and ultimate cure.

In radiation therapy clinic, multi-modality imaging is increasingly employed to assist target

delineation and localization, aiming to have a better local control of cancer (Nestle, et al,

2

Biomedical Imaging

2009). Radiation therapy (RT) contains three basic components: treatment simulation,

mm and time requirement of 15-20 minutes is sufficiently accurate and fast to meet the

treatment planning and treatment delivery (Song & Li, 2008). Simulation is to imaging a

clinical challenges of increasing utilization of multi-modality images in planning, increasing

patient at treatment condition for planning, based on which the treatment is delivered. In

adoption of image-guided delivery, and increasing throughput of patient treatments.

image-based planning, multimodality images, including CT, MRI and PET, can be registered

and used to define the target volume and location within the anatomy (Schad et al, 1987,

Chen & Pelizzari, 1989). In image-guided delivery, on-site imaging which provides patient’s

positioning image, is used to register to the planning CT image for accurate patient setup, so

that the target is treated as planned (Jaffray, et al, 2007).

Therefore, in both diagnostic and therapeutic imaging, image registration is critical for a

successful clinical application. Beyond the 3D space, 4D (3D+time) biomedical imaging has

become an emerging clinical research field, and some procedures have been adopted in the

clinic, such as 4DCT (Li et al, 2008a). Motion is inevitably present during imaging as well as

therapeutic processes, including respiratory, cardiac, digestive and muscular motions,

causing image blurring and target relocation. 4D medical imaging aims to minimize the

motion artefact and 4DRT aims to track and compensate for the target motion. Facing the

challenge of patient’s motion and change along the time, deformable image registration has

been intensively studied (Hill, et al, 2001, Pluim et al, 2003, Li et al, 2008b). Although it

Fig. 1. Illustration of two common means of image alignment based on 2D planar views

remains as challenging topic, it will be only discussed briefly where it is needed, as it is not

(Only one of the axial slices is shown, and the sagittal and coronal series are not shown).

the main focus of this chapter.

The 3D visual representation or volumetric visualization (Udupa, 1999, Schroeder, et al,

2004) has recently been applied to evaluate the volumetric alignment of two or more 3D

1.2 Manual Image Registration

images (Xie, et al, 2004, Li, et al, 2005, 2007, 2008b and 2008c). This 3D volumetric image

Manual or interactive image registration is guided by visual indication of image alignment.

registration (3DVIR) technique aims to solve most of the problems associated with the

The conventional visual representation of an 3D images is 2D-based, three orthogonal

conventional 2D fusion technique by providing a fundamentally different, volumetric visual

planar views of cross-section of the volumetric image (West, et al, 1997, Fitzpatrick, et al,

representation of multimodality images. This volumetric technique has been successfully

1998). Here the discussion will be focused on anatomy-based image registration, rather than

designed, developed and validated, while it is still relatively new to the medical field and

fiducial-based (such as superficial or implanted markers) or coordinate-based (such as

has not been widely adopted as an alternative (superior) to the conventional 2D visual

combined PET/CT system). All clinical treatment planning systems utilize this visual

fusion technique. Two of the major obstacles for the limited clinical applications are that (1)

representation for checking and adjusting the alignment of two images. In details, there are

from 2D to 3D visualization, the clinical practitioners have to be retrained to adapt

several means to achieve the visual alignment verification: (1) the chess-box display of two

themselves to this new technique, and (2) this technique has not yet been commercially

images in alternate boxes; (2) the simultaneous display of two mono-coloured images; and

available to the clinic.

(3) the superimposed display of the two images with an adjustable weighting factor. Fig. 1

illustrates the first two of the three basic visualization methods.

1.3 Automatic Image Registration

The 2D visual-based fusion technique has been developed, validated and adopted for

Automatic image registration can improve the efficiency and accuracy of the visual-based

biomedical research as well as clinical practice (Hibbard, et al, 1987, Chen, et al, 1987,

manual fusion technique. There are three major components in any automatic image

Hibbard & Hawkins, 1988, Pelizzari, et al, 1989, Toga & Banerjee, 1993, Maintz & Viergever,

registration, including (1) registration criterion; (2) transformation and interpolation; and (3)

1998, Hill, et al, 2001). Throughout the past three decades, this technique has evolved and

optimization. These three components are independent of one another, so that they can be

become a well developed tool to align 3D images in the clinic. Multi-modality image

freely recombined for an optimal outcome in a particular clinical application. Here again,

registration is required (Schad et al, 1987, Pelizzari, et al, 1989) as more medical imaging is

the discussion will focus on anatomy-based rigid image registration, rather than fiducial-

available to the clinic. However, reports have shown that this well established technique

based or coordinate-based registration.

may suffer from (1) large intra- and inter-observer variability; (2) the dependency of user’s

cognitive ability; (3) limited precision by the resolution of imaging and image display; and

Before mutual information criterion (negative cost function) was developed in 1995 (Viola &

(4) time consuming in verifying and adjusting alignment in three series of planar views in

Wells, 1995), other algorithms were utilized, such as Chamfer surface matching criterion

three orthogonal directions (Fitzpatrick, et al, 1998, Vaarkamp, 2001). These findings have

(Borgefors, 1988, van Herk & Kooy, 1994) or voxel intensity similarity criterion (Venot, et al,

become a concern whether this 2D visual-based fusion technique with an accuracy of 1-3

1984). Mutual information is fundamentally derived from information theory and has been

index-11_1.png

Volumetric

Image Registration of Multi-modality Images of CT, MRI and PET

3

2009). Radiation therapy (RT) contains three basic components: treatment simulation,

mm and time requirement of 15-20 minutes is sufficiently accurate and fast to meet the

treatment planning and treatment delivery (Song & Li, 2008). Simulation is to imaging a

clinical challenges of increasing utilization of multi-modality images in planning, increasing

patient at treatment condition for planning, based on which the treatment is delivered. In

adoption of image-guided delivery, and increasing throughput of patient treatments.

image-based planning, multimodality images, including CT, MRI and PET, can be registered

and used to define the target volume and location within the anatomy (Schad et al, 1987,

Chen & Pelizzari, 1989). In image-guided delivery, on-site imaging which provides patient’s

positioning image, is used to register to the planning CT image for accurate patient setup, so

that the target is treated as planned (Jaffray, et al, 2007).

Therefore, in both diagnostic and therapeutic imaging, image registration is critical for a

successful clinical application. Beyond the 3D space, 4D (3D+time) biomedical imaging has

become an emerging clinical research field, and some procedures have been adopted in the

clinic, such as 4DCT (Li et al, 2008a). Motion is inevitably present during imaging as well as

therapeutic processes, including respiratory, cardiac, digestive and muscular motions,

causing image blurring and target relocation. 4D medical imaging aims to minimize the

motion artefact and 4DRT aims to track and compensate for the target motion. Facing the

challenge of patient’s motion and change along the time, deformable image registration has

been intensively studied (Hill, et al, 2001, Pluim et al, 2003, Li et al, 2008b). Although it

Fig. 1. Illustration of two common means of image alignment based on 2D planar views

remains as challenging topic, it will be only discussed briefly where it is needed, as it is not

(Only one of the axial slices is shown, and the sagittal and coronal series are not shown).

the main focus of this chapter.

The 3D visual representation or volumetric visualization (Udupa, 1999, Schroeder, et al,

2004) has recently been applied to evaluate the volumetric alignment of two or more 3D

1.2 Manual Image Registration

images (Xie, et al, 2004, Li, et al, 2005, 2007, 2008b and 2008c). This 3D volumetric image

Manual or interactive image registration is guided by visual indication of image alignment.

registration (3DVIR) technique aims to solve most of the problems associated with the

The conventional visual representation of an 3D images is 2D-based, three orthogonal

conventional 2D fusion technique by providing a fundamentally different, volumetric visual

planar views of cross-section of the volumetric image (West, et al, 1997, Fitzpatrick, et al,

representation of multimodality images. This volumetric technique has been successfully

1998). Here the discussion will be focused on anatomy-based image registration, rather than

designed, developed and validated, while it is still relatively new to the medical field and

fiducial-based (such as superficial or implanted markers) or coordinate-based (such as

has not been widely adopted as an alternative (superior) to the conventional 2D visual

combined PET/CT system). All clinical treatment planning systems utilize this visual

fusion technique. Two of the major obstacles for the limited clinical applications are that (1)

representation for checking and adjusting the alignment of two images. In details, there are

from 2D to 3D visualization, the clinical practitioners have to be retrained to adapt

several means to achieve the visual alignment verification: (1) the chess-box display of two

themselves to this new technique, and (2) this technique has not yet been commercially

images in alternate boxes; (2) the simultaneous display of two mono-coloured images; and

available to the clinic.

(3) the superimposed display of the two images with an adjustable weighting factor. Fig. 1

illustrates the first two of the three basic visualization methods.

1.3 Automatic Image Registration

The 2D visual-based fusion technique has been developed, validated and adopted for

Automatic image registration can improve the efficiency and accuracy of the visual-based

biomedical research as well as clinical practice (Hibbard, et al, 1987, Chen, et al, 1987,

manual fusion technique. There are three major components in any automatic image

Hibbard & Hawkins, 1988, Pelizzari, et al, 1989, Toga & Banerjee, 1993, Maintz & Viergever,

registration, including (1) registration criterion; (2) transformation and interpolation; and (3)

1998, Hill, et al, 2001). Throughout the past three decades, this technique has evolved and

optimization. These three components are independent of one another, so that they can be

become a well developed tool to align 3D images in the clinic. Multi-modality image

freely recombined for an optimal outcome in a particular clinical application. Here again,

registration is required (Schad et al, 1987, Pelizzari, et al, 1989) as more medical imaging is

the discussion will focus on anatomy-based rigid image registration, rather than fiducial-

available to the clinic. However, reports have shown that this well established technique

based or coordinate-based registration.

may suffer from (1) large intra- and inter-observer variability; (2) the dependency of user’s

cognitive ability; (3) limited precision by the resolution of imaging and image display; and

Before mutual information criterion (negative cost function) was developed in 1995 (Viola &

(4) time consuming in verifying and adjusting alignment in three series of planar views in

Wells, 1995), other algorithms were utilized, such as Chamfer surface matching criterion

three orthogonal directions (Fitzpatrick, et al, 1998, Vaarkamp, 2001). These findings have

(Borgefors, 1988, van Herk & Kooy, 1994) or voxel intensity similarity criterion (Venot, et al,

become a concern whether this 2D visual-based fusion technique with an accuracy of 1-3

1984). Mutual information is fundamentally derived from information theory and has been

4

Biomedical Imaging

extensively discussed in the literature (Hill, et al, 2001, Pluim, et al, 2003). It is worthwhile to

difficulty or with high accuracy requirement. We have found that pairing automatic MMI

mention that among existing criteria the common features in two different modality images

registration and the 3DVIR serves the best in terms of registration speed and outcome.

are best described by the mutual information, which can serve as the registration cost

The advantage of hybridized image registration is that it will take the advantage of multiple

function for maximization to achieve multi-modality image registration.

image processing techniques. Image segmentation/classification can extract more reliable

features from the original image to enhance image registration with the more informative

The transformation and interpolation are mathematical operations of the images. For rigid

features. Image (volumetric) visualization can enhance image registration, if a classified

image registration, only six degrees of freedom (three rotational and three translational) are

reliable anatomy is visualized and utilized as the registration landmark. Therefore, hybrid

in the transformation and the transformed voxels are assigned through interpolation (linear,

image registration remains a focus of clinical research (Li, et al, 2008b). Although feature

nearest neighbour, or Spline). For deformable image registration, however, the number of

extraction is often application specific and few algorithms can be employed across the

degree of freedom is dramatically increased, since all voxels are allowed to move (deform)

spectrum of all imaging modalities, hybrid image registration, such as the 3DVIR, has

independently and therefore the number of variables would be up to three times of the total

shown its promise to resolve particular clinical problems that require high accuracy.

number of voxels in an image. As a consequence, the performance of deformable image

registration becomes one of the bottlenecks, despite that several simplified algorithms have

been studied to address this challenging problem (Pluim et al, 2003, Li et al, 2008a & 2008b).

1.5 Visual Verification of Registration

Although automatic rigid image r