Medical Image Processing: Techniques and Applications

397 Pages·2011·5.856 MB·English

by  Geoff Dougherty (ed.)

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Geoff Dougherty
Editor
Medical Image Processing
Techniques and Applications
123
Editor
GeoffDougherty
AppliedPhysicsandMedicalImaging
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ISSN1618-7210
ISBN978-1-4419-9769-2 e-ISBN978-1-4419-9779-1
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To mymother,AdelineMaudDougherty,
andmyfather,HarryDougherty(who left
uson 17thNovember 2009)

Preface
The field of medical imaging advances so rapidly that all of those working in it,
scientists, engineers, physicians, educators, and others, need to frequently update
their knowledge to stay abreast of developments. While journals and periodicals
play a crucial role in this, more extensive, integrative publications that connect
fundamentalprinciplesandnewadvancesinalgorithmsandtechniquestopractical
applicationsareessential.Suchpublicationshaveanextendedlifeandformdurable
links in connecting past procedures to the present, and present procedures to the
future.Thisbookaimstomeetthischallengeandprovideanenduringbridgeinthe
everexpandingfieldofmedicalimaging.
This book is designed for end users in the field of medical imaging, who
wish to update their skills and understanding with the latest techniques in image
analysis.Thebookemphasizestheconceptualframeworkofimageanalysisandthe
effective use of image processing tools. It is designed to assist cross-disciplinary
dialog both at graduate and at specialist levels, between all those involved in the
multidisciplinary area of digital image processing, with a bias toward medical
applications.Itsaimistoenablenewenduserstodrawontheexpertiseofexperts
acrossthespecializationgap.
To accomplish this, the book uses applicationsin a variety of fields to demon-
strate and consolidate both specific and general concepts, and to build intuition,
insight,andunderstanding.Itpresentsadetailedapproachtoeachapplicationwhile
emphasizing the applicability of techniques to other research areas. Although the
chapters are essentially self-contained, they reference other chapters to form an
integrated whole. Each chapter uses a pedagogicalapproachto ensure conceptual
learningbeforeintroducingspecifictechniquesand“tricksofthetrade”.
The book is unified by the theme foreshadowed in the title “Medical Image
Processing:TechniquesandApplications.”Itconsistsofacollectionofspecialized
topics, each presented by a specialist in the field. Each chapter is split into
sectionsand subsections, and beginswith an introductionto the topic,method,or
technology.Emphasisisplacednotonlyonthebackgroundtheorybutalso onthe
practicalaspects of the method, the details necessary to implementthe technique,
vii
viii Preface
and limits of applicability. The chapter then introduces selected more advanced
applicationsofthetopic,method,ortechnology,leadingtowardrecentachievements
and unresolvedquestionsin a mannerthat can be understoodby a reader with no
specialistknowledgeinthatarea.
Chapter1,byDougherty,presentsabriefoverviewofmedicalimageprocessing.
He outlinesa numberofchallengesandhighlightsopportunitiesforfurtherdevel-
opment.
A number of image analysis packages exist, both commercial and free, which
makeuse oflibrariesof routinesthatcan be assembled/mobilized/concatenatedto
automate an image analysis task. Chapter 2, by Luengo, Malm, and Bengtsson,
introduces one such package, DIPimage, which is a toolbox for MatLab that
incorporatesaGUIforautomaticimagedisplayandaconvenientdrop-downmenu
of common image analysis functions. The chapter demonstrates how one can
quicklydevelopasolutiontoautomateacommonassessmenttasksuchascounting
cancerouscellsinaPapsmear.
Segmentationisoneofthekeytoolsinmedicalimageanalysis.Themainappli-
cationofsegmentationis in delineatinganorganreliably,quickly,andeffectively.
Chapter 3, by Couprie, Najman and Talbot, presents very recent approaches that
unifypopulardiscretesegmentationmethods.
Deformable models are a promising method to handle the difficulties in seg-
menting images that are contaminated by noise and sampling artifact. The model
is represented by an initial curve (or surface in three dimensions (3D)) in the
imagewhichevolvesundertheinfluenceofinternalenergy,derivedfromthemodel
geometry, and an external force, defined from the image data. Segmentation is
thenachievedbyminimizingthe sumofthese energies,whichusuallyresultsin a
smoothcontour.InChapter4,Alfiansyahpresentsareviewofdifferentdeformable
modelsandissuesrelatedtotheirimplementations.Hepresentssomeexamplesof
thedifferentmodelsusedwithnoisymedicalimages.
Over the past two decades, many authorshave investigatedthe use of MRI for
the analysis of body fat and its distribution. However, when performedmanually,
accurate isolation of fat in MR images can be an arduous task. In order to
alleviate this burden,numeroussegmentation algorithmshave been developedfor
the quantificationof fatin MR images.These includea numberofautomatedand
semi-automatedsegmentationalgorithms.InChapter5,CostelloandKennydiscuss
some of the techniques and models used in these algorithms, with a particular
emphasisontheirapplicationandimplementation.Thepotentialimpactofartifacts
suchasintensityinhomogeneities,partialvolumeeffect(PVE),andchemicalshift
artifactsonimagesegmentationarealsodiscussed.
An increasing portion of medical imaging problems concern thin objects, and
particularlyvesselfiltering,segmentation,andclassification.Exampleapplications
include vascular tree analysis in the brain, the heart, or the liver, the detection of
aneurysms, stenoses, and arteriovenous malformations in the brain, and coronal
treeanalysisinrelationtothepreventionofmyocardialinfarction.Thin,vessel-like
objectsaremoredifficulttoprocessingeneralthanmostimagesfeatures,precisely
becausetheyarethin.Theyarepronetodisappearwhenusingmanycommonimage
Preface ix
analysis operators, particularly in 3D. Chapter 6, by Tankyevych, Talbot, Passat,
Musacchio, and Lagneau, introduces the problem of cerebral vessel filtering and
detectionin3Danddescribesthestateoftheartfromfilteringtosegmentation,using
localorientation,enhancement,localtopology,andscaleselection.Theyapplyboth
linearandnonlinearoperatorstoatlascreation.
Automated detection of linear structures is a common challenge in many
computervisionapplications.Wheresuchstructuresoccurinmedicalimages,their
measurement and interpretation are important steps in certain clinical decision-
making tasks. In Chapter 7, Dabbah, Graham, Malik, and Efron discuss some of
the well-knownlinear structure detectionmethodsused in medicalimaging.They
describeaquantitativemethodforevaluatingtheperformanceofthesealgorithmsin
comparisonwiththeirnewlydevelopedmethodfordetectingnervefibersinimages
obtainedusinginvivocornealconfocalmicroscopy(CCM).
Advances in linear feature detection have enabled new applications where the
reliabletracingofline-likestructuresiscritical.Thisincludesneuriteidentification
inimagesofbraincells,thecharacterizationofbloodvessels,thedelineationofcell
membranes,andthesegmentationofbacteriaunderhighresolutionphasecontrast
microscopy. Linear features represent fundamental image analysis primitives. In
Chapter 8, Domanski, Sun, Lagerstrom, Wang, Bischof, Payne, and Vallotton
introducethealgorithmsforlinearfeaturedetection,considerthepreprocessingand
speed options, and show how such processing can be implemented conveniently
usingagraphicaluserinterfacecalledHCA-Vision.Thechapterdemonstrateshow
thirdpartiescanexploitthesenewcapabilitiesasinformedusers.
Osteoporosisisadegenerativediseaseofthebone.Theaveragingnatureofbone
mineral density measurement does not take into account the microarchitectural
deterioration within the bone. In Chapter 9, Haidekker and Dougherty consider
methodsthatallowthedegreeofmicroarchitecturaldeteriorationoftrabecularbone
to be quantified.These havethe potentialto predictthe load-bearingcapabilityof
bone.
InChapter10,AdamandDoughertydescribetheapplicationofmedicalimage
processingtotheassessmentandtreatmentofspinaldeformity,withafocusonthe
surgical treatment of idiopathic scoliosis. The natural history of spinal deformity
andcurrentapproachestosurgicalandnonsurgicaltreatmentarebrieflydescribed,
followed by an overview of current clinically used imaging modalities. The key
metricscurrentlyused to assess the severity and progressionof spinaldeformities
from medical images are presented, followed by a discussion of the errors and
uncertainties involved in manual measurements. This provides the context for an
analysisofautomatedandsemi-automatedimageprocessingapproachestomeasure
spinalcurveshapeandseverityintwoandthreedimensions.
In Chapter 11, Cree and Jelinek outline the methods for acquiring and pre-
processing of retinal images. They show how morphological,wavelet, and fractal
methodscanbeusedtodetectlesionsandindicatethefuturedirectionsofresearch
inthisarea.
Theappearanceoftheretinalbloodvesselsisanimportantdiagnosticindicator
for much systemic pathology. In Chapter 12, Iorga and Dougherty show that the