Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

287 Pages·2016·9.96 MB·English

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Electronic Theses and Dissertations
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Ahmed Abd-Elrahman Elnakib
University of Louisville
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Elnakib, Ahmed Abd-Elrahman, "Developing advanced mathematical models for detecting abnormalities in
2D/3D medical structures." (2013). Electronic Theses and Dissertations. Paper 402.
https://doi.org/10.18297/etd/402
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DEVELOPING ADVANCED MATHEMATICAL MODELS FOR
DETECTING ABNORMALITIES IN 2D/3D MEDICAL
STRUCTURES
By
AhmedAbd-ElrahmanElnakib
M.Sc.,ECE,MansouraUniversity,2007
ADissertation
SubmittedtotheFacultyofthe
J.B.SpeedSchooloftheUniversityofLouisville
inPartialFulfillmentoftheRequirements
fortheDegreeof
DoctorofPhilosophy
DepartmentofElectricalandComputerEngineering
UniversityofLouisville
Louisville,Kentucky
December2013

DEVELOPING ADVANCED MATHEMATICAL MODELS FOR
DETECTING ABNORMALITIES IN 2D/3D MEDICAL
STRUCTURES
By
AhmedAbd-ElrahmanElnakib
M.Sc.,ECE,MansouraUniversity,2007
ADissertationApprovedon
bytheFollowingReadingandExaminationCommittee:
AymanEl-Baz,Ph.D.,Dissertation
Director
TamerInanc,Ph.D.,Co-Advisor
ManuelCasanova,Ph.D.
OlfaNasraoui,Ph.D.
KarlaWelch,Ph.D.
CindyHarnett,Ph.D.
ii
DEDICATION
Thisdissertationisdedicatedtomymother,myfather,andmywifefortheirlove,
patience,andsupportduringthecompletionofthisendeavor.
iii
ACKNOWLEDGMENTS
In the Name of Allah, the most beneficent, the most merciful. All deepest
thanksareduetoAllah,thecompassionatefortheuncountablegiftsgiventome.
I would like to express my sincere gratitude to Professor El-Baz, my dis-
sertation advisor, for the immeasurable amount of support and guidance he has
providedthroughoutthisstudy. Hegavemetheopportunitytoworkinhisdistin-
guished research group and provided me with an exciting working environment
that facilitate many opportunities to develop new ideas and working on promis-
ing applications. Dr. El-Baz is always willing to give, share, and appreciate. I
knew that I could always expect full credit for my accomplishments. He always
supported me with novel ideas that helped me to publish my works in the top in-
ternational conference and journal papers. It is worthy to mention that Professor
El-BazisagreatadvisoraswellasagoodfriendwhohelpsanyPh.D.studentwho
workswithhiminhighenergylevel. Icannotthankhimenough,andshallremain
gratefulforallhehasdone.
I would like to thank Professor Garth Beache of the Department of Radiol-
ogy of University of Louisville Hospital for spending much of his valuable time
guiding my work in automatic detection of cardiac damage tissue and automatic
segmentationofheartcavityandothercardiacstructures,commentingonitsqual-
ity,andformanysuggestionsonhowtomakethetoolsdevelopedmoreapplicable
inthesettingofmedicalresearchandclinicaluse.
iv
IwouldliketothankProfessorManualCasanovaoftheDepartmentofPsy-
chiatryofUniversityofLouisvilleforspendingmuchofhisvaluabletimeguiding
my work in dyslexia diagnostics and automatic segmentation of brain structures,
commenting on its quality, and for many suggestions on how to make the tools
developedmoreapplicableinthesettingofmedicalresearchandclinicaluse.
IwouldliketoexpressmydeepestgratitudetoProfessorTamerInanc,Pro-
fessor Olfa Nasraoui, Professor Karla Welch, and Professor Cindy Harnett for be-
ingonmydissertationcommitteewithenthusiasm.
I would like to thank all the team members of the BioImaging Laboratory
attheUniversityofLouisville,FahmiKhalifa,AhmedSoliman,MatthewNitkzen,
Hisham Sliman, Amir Al-Ansary, and Mahmoud Osman. I am very proud to be
amongthisstrongteamwhereallmembershavebothhighspiritanddistinguished
merits. Weneverachievesuccesswithoutthecooperationbetweentheteammem-
bersinsharingtheideasandthoughtsandsincerelysupportingeachother.
Lastbutnotleast,IwouldliketoexpressmysincerethankstomyfatherMr.
Abd-ElrahmanElnakib,mymotherMrs. AmalElnakib,mywifeMrs. EtharEliwa,
mybrothersMr. MohameedElnakib,Mr. SeifElnakib,andMr. YasserElnakib,my
sisters Mrs. Gehad Elnakib, Mrs. Eman Elnakib, Mrs. Fatma Elnakib, and Ms.
Alaa Elnakib, my daughter Gehad Elnakib, and my son Yusuf Elnakib. Nothing
couldhavebeenpossiblewithouttheirultimatesupportandlove.
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ABSTRACT
DEVELOPINGADVANCEDMATHEMATICALMODELSFORDETECTING
ABNORMALITIESIN2D/3DMEDICALSTRUCTURES
AhmedAbd-ElrahmanElnakib
December05,2013
Detectingabnormalitiesintwo-dimensional(2D)andthree-dimensional(3D)
medicalstructuresisamongthemostinterestingandchallengingresearchareasin
the medical imaging field. Obtaining the desired accurate automated quantifica-
tion of abnormalities in medical structures is still very challenging. This is due to
a large and constantly growing number of different objects of interest and asso-
ciated abnormalities, large variations of their appearances and shapes in images,
differentmedicalimagingmodalities,andassociatedchangesofsignalhomogene-
ity and noise for each object. The main objective of this dissertation is to address
these problems and to provide proper mathematical models and techniques that
are capable of analyzing low and high resolution medical data and providing an
accurate,automatedanalysisoftheabnormalitiesinmedicalstructuresintermsof
theirarea/volume,shape,andassociatedabnormalfunctionality.
This dissertation presents different preliminary mathematical models and
techniques that are applied in three case studies: (i) detecting abnormal tissue in
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the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac
magneticresonanceimages(MRI),(ii)detectinglocalcardiacdiseasesbasedones-
timatingthefunctionalstrainmetricfromcardiaccineMRI,and(iii)identifyingthe
abnormalities in the corpus callosum (CC) brain structure—the largest fiber bun-
dle that connects the two hemispheres in the brain—for subjects that suffer from
developmental brain disorders. For detecting the abnormal tissue in the heart,
a graph-cut mathematical optimization model with a cost function that accounts
for the object’s visual appearance and shape is used to segment the the inner cav-
ity. The model is further integrated with a geometric model (i.e., a fast marching
levelsetmodel)tosegmenttheouterborderofthemyocardialwall(theLV).Then
the abnormal tissue in the myocardium wall (also called dead tissue, pathological
tissue, or infarct area) is identified based on a joint Markov-Gibbs random field
(MGRF) model of the image and its region (segmentation) map that accounts for
the pixel intensities and the spatial interactions between the pixels. Experiments
with real in-vivo data and comparative results with ground truth (identified by a
radiologist)andotherapproachesshowedthattheproposedframeworkcanaccu-
ratelydetectthepathologicaltissueandcanprovideusefulmetricsforradiologists
andclinicians.
ToestimatethestrainfromcardiaccineMRI,anovelmethodbasedontrack-
ing the LV wall geometry is proposed. To achieve this goal, a partial differen-
tial equation (PDE) method is applied to track the LV wall points by solving the
Laplace equation between the LV contours of each two successive image frames
overthe cardiaccycle. The mainadvantage of the proposedtracking method over
traditionaltexture-basedmethodsisitsabilitytotrackthemovementandrotation
of the LV wall based on tracking the geometric features of the inner, mid-, and
outer walls of the LV. This overcomes noise sources that come from scanner and
heartmotion.
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ToidentifytheabnormalitiesintheCCfrombrainMRI,theCCsarealigned
using a rigid registration model and are segmented using a shape-appearance
model. Then, they are mapped to a simple unified space for analysis. This work
introducesanovelcylindricalmappingmodel,whichisconformal(i.e.,onetoone
transformationandbijective),thatenablesaccurate3DshapeanalysisoftheCCin
the cylindrical domain. The framework can detect abnormalities in all divisions
of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole
3D analysis of the CC abnormalities instead of only area-based analysis as done
bypreviousgroups. Theinitialclassificationresultsbasedonthecenterlinelength
andCCthicknesssuggestthattheproposedCCshapeanalysisisapromisingsup-
plementtothecurrenttechniquesfordiagnosingdyslexia.
The proposed techniques in this dissertation have been successfully tested
on complex synthetic and MR images and can be used to advantage in many of
today’s clinical applications of computer-assisted medical diagnostics and inter-
vention.
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