Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume II

380 Pages·2011·9.14 MB·English

by  Ahmed Elnakib

Multi Modality State-of-the-Art Medical Image
Segmentation and Registration Methodologies
.
Ayman El-Baz Rajendra Acharya U
l l
Andrew F. Laine Jasjit S. Suri
l
Editors
Multi Modality State-of-
the-Art Medical Image
Segmentation and
Registration Methodologies
Volume II
Editors RajendraAcharyaU
AymanEl-Baz NgeeAnnPolytechnic
UniversityofLouisville SchoolofEngineering
DepartmentofBioengineering ClementiRoad535
Louisville 599489Singapore
USA Blk7Level2
[email protected] Singapore
[email protected]
AndrewF.Laine JasjitS.Suri
ColumbiaUniversity BiomedicalTechnologies,Inc.
DepartmentofBiomedicalEngineering Denver,CO,USA
NewYork GlobalBiomedicalTechnologies,Inc.
USA California,USA
(Aff.)IdahoStateUniversity
Pocatello,ID,USA
[email protected]
ISBN978-1-4419-8203-2 e-ISBN978-1-4419-8204-9
DOI10.1007/978-1-4419-8204-9
SpringerNewYorkDordrechtHeidelbergLondon
LibraryofCongressControlNumber:2011924210
# SpringerScienceþBusinessMedia,LLC2011
Allrightsreserved.Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten
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Theuseinthispublicationoftradenames,trademarks,servicemarks,andsimilarterms,eveniftheyare
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Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
AymanEl-Bazwouldliketodedicatethisbookto
hiswife,daughter,son,mother,andfather.
RajendraAcharyaUwouldliketodedicatethisbookto
hisstudents,collaborators,andcolleagues.
AndrewF.Lainewouldliketodedicatethisbookto
hislatebelovedfatherJasonLaine.
JasjitS.Suriwouldliketodedicatethisbookto
hisstudentsandcollaboratorsallovertheworld.
.
Preface
Segmentation of medical images in 2-D, 3-D, and 4-D has taken new dimension.
Thisismainlybecauseforreal-timeimageguidanceindiagnosisandtherapeutics.
Theapplicationofimage/volumeregistrationisprominentlyseeninreconstruction,
multimodality fusion, and now propagating in atlas-based analysis. The focus of
thisbook is tosharethe applicationofcombined segmentationandregistrationin
medicalimaging.
Someofthecuttingedgetopicsinsegmentationcoveredinthebookaregraph
cut, energy minimization methods, cine loop processing in cardiac applications,
parametric and geometric deformable models in combination with principal com-
ponent analysis, breast mass classification, classification of thyroid lesions into
benignandmalignantusingvasculatureproperties,classificationofautisticversus
nonautisticbraindatasets,andgeometricmodelingforprosthesisdesign.Someof
theadvancedtopicsinimage/volumeregistrationareoptimizersforimageregistra-
tion,suchasAmoeba,conjugategradient,andgradient-basedmethodsandtechni-
ques for alignment of images of a textured object with a given prototype,
multimodality approaches to image registration. The book also presents united
variation frameworks where prior segmentation information can be seamlessly
integrated into nonrigid registration procedures for brain structure registration.
Thedetaillayoutofthebookisasfollows.
Chapter1givesabriefoverviewofthemostpopularmedicalimagesegmenta-
tion techniques and discusses their capabilities and basic advantages and limita-
tions.Moreover,thestate-of-the-arttechniquesofthelastdecadeareoutlined.
Chapter 2 presented a graph cut-based segmentation approach to segment
cerebral white matter (CWM) from magnetic resonance images (MRIs). This
approach is based on using a probabilistic variation of the traditional graph cut
algorithm with an improved parameter selection mechanism for the energy func-
tion,tobeoptimizedinagraphcutproblem.
Chapter3introducedanewframeworktoanalyzenoncontrastagentcinecardiac
magneticresonance(CMR).Theframeworkincludestwoautomatedalgorithms(1)
to segment the inner and outer borders (or walls) from the surrounding tissues in
cine CMR images based on using a new stochastic speed function to control the
evolution of geometric deformable model, and (2) to estimate the local wall
thickness, and thickening function indexes based on solving Laplace’s equation
vii
viii Preface
inthesegmentedwalltofindpoint-to-pointcorrespondencesbetweentheinnerand
outerborderofthesegmentedwall.
Chapter 4 presented a novel shape-based segmentation approach using learned
prior and current appearance model. The target shapes are approximated directly
with linear combinations of distance vectors describing positions of the mutually
alignedtrainingshapeswithrespecttotheircommoncentroid.Suchavectorspace
is now closed with respect to the linear operations and it is of much smaller
dimensionality thanthe2-Ddistancemaps.Priorknowledgeofvisualappearance
of the object is represented by Gibbs energies of its gray levels. To accurately
separatetheobjectfromitsbackground,eachcurrentempiricalmarginalprobabili-
ty distribution of gray values within a deformable boundary is also approximated
withanadaptivelinearcombinationofdiscreteGaussians.Boththeshape/appear-
ancepriorsandthecurrentprobabilisticappearancedescriptioncontrolthebound-
aryevolution.
An improved level set-based mass segmentation is presented in Chap. 5 to
classifythesegmentedmasstomalignantorbenignbasedontheestimatedtexture
and morphological features. The new mass segmentation algorithm is based on a
new energy functional, which combines energy terms from several traditional
algorithms.InChap.6,animage-basedframeworkispresentedtodiagnosesingle
thyroidnodule.Thisimage-basedapproachisbasedonthreemainsteps:(1)image
preprocessing;(2)morphologicalapertureforcontrastagentbubblesdetection;(3)
thresholdingand3-Dlesionreconstruction.Chapter7coversanovelframeworkto
analyze the cortex of the patient with autism using spherical harmonics analysis.
Chapter 8 covers state-of-the-art approaches related to surface reconstruction and
geometric methods that have been used for digital prosthesis design. Chapter 9
coversthenecessarybackgroundinformationrequiredtounderstandmedicalimage
registration, the basic tools required to implement registration algorithms, and to
demonstrate a complete application for various types of registration between
differentmodalitiesusingfreelyavailableandmaintainedsoftware.
Chapter 10 presented a novel approach to align an image of a textured object
with a given prototype using a new similarity measure. Visual appearance of the
images, after equalizing their signals, is modeled with a Markov–Gibbs random
field(MGRF)withpairwiseinteraction.Similaritytotheprototypeismeasuredby
a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs
derived automatically from the prototype. Chapter 11 covers three registration
methods that address some of the technical choices that are part of specific
engineeringsolutions.These, while driven by concreteclinical needs, may gener-
alize well to similar problems in other domains requiring image registration.
Chapter 12 presented registration-based framework to estimate ocular surface
temperature. Chapter 13 presented simultaneous segmentation and registration
approachtotrackbrainmorphologicalchangesinMRIs.
Contents
1 MedicalImageSegmentation:ABriefSurvey ......................... 1
AhmedElnakib,GeorgyGimel’farb,JasjitS.Suri,andAymanEl-Baz
2 CerebralWhiteMatterSegmentationusingProbabilistic
GraphCutAlgorithm ................................................... 41
MainakSen,AshishK.Rudra,AnandaS.Chowdhury,
AhmedElnakib,andAymanEl-Baz
3 ANewImage-BasedFrameworkforAnalyzingCineImages ........ 69
FahmiKhalifa,GarthM.Beache,GeorgyGimel’farb,
GuruprasadA.Giridharan,andAymanEl-Baz
4 MedicalImagesSegmentationUsingLearnedPriors ................. 99
AymanEl-BazandGeorgyGimel’farb
5 ClassificationofBreastMassinMammographywithan
ImprovedLevelSetSegmentationbyCombiningMorphological
FeaturesandTextureFeatures ........................................ 119
JinshanTangandXiaomingLiu
6 SegmentationandSkeletonizationof3DContrastEnhanced
UltrasoundImagesfortheCharacterizationofSingle
ThyroidNodule ......................................................... 137
FilippoMolinari,AliceMantovani,MaurilioDeandrea,PaoloLimone,
RajendraAcharyaU,RobertoGarberoglio,andJasjitS.Suri
7 Shape-BasedDetectionofCortexVariabilityforMoreAccurate
DiscriminationBetweenAutisticandNormalBrains ................ 161
MatthewNitzken,ManuelF.Casanova,FahmiKhalifa,
GuelaSokhadze,andAymanEl-Baz
ix