Towards Robust and Accurate Image Registration by Incorporating Anatomical and Appearance ...

165 Pages·2014·1.97 MB·English

Towards Robust and Accurate Image Registration
by Incorporating Anatomical and Appearance
Priors
LU YONGNING
(B. Eng., National University of Singapore)
A thesis submitted for the degree of
Doctor of Philosophy
NUS Graduate School for Integrative Sciences and Engineering
NATIONAL UNIVERSITY OF SINGAPORE
2014

Declaration
I hereby declare that the thesis is my original
work and it has been written by me in its entirety.
I have duly acknowledged all the sources of
information which have been used in the thesis.
This thesis has also not been submitted for any
degree in any university previously.
Signed:
LU YONGNING
Date:
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This thesis is dedicated to
all the people – who never stopped having faith in me
my parents – who raised me and supported my
education, for your love and sacrifices
my wife – who is so understanding and supportive
along the journey
my friends – whom I am so grateful to have in my life
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Acknowledgements
I would like to thank all my supervisors, Assoc. Prof. Ong Sim
Heng, Dr. Sun Ying, Dr. Liao Rui, Dr. Zhang Li and members of
my Thesis Advisory Committee – Prof. Ge Shuzhi, Assoc. Prof.
Lee Chi Hang, for their guidance and support, without which my
research wouldnotbeguidedsowell and carried outsmoothly.
I would like to thank Mr. Francis Hoon, laboratory officer at Vi-
sion and Machine Learning Laboratory, for the assistance during
my PhD study. Special thanks to my friends and colleagues in the
lab Dr. Ji Dongxu, Dr. Wei Dong, Dr. Cao Chuqing, for your
encouragementand companyalongmyPhD journey.
I would also like to thank Siemens Corporate Research and Tech-
nology, for offering an internship opportunity to me. I have been
enlightened during the internship for both the academic work and
life.
Finally, I would liketo thank NUS Graduate School for Integrative
Sciences and Engineering (NGS) for awarding me the NGS Schol-
arship. Manythanksgoestothedirectors,mangersandstaffatNGS
fortheirhelp and support.
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Summary
Imageregistrationisoneofthefundamentalcomputervisionprob-
lems, with applications ranging from motion modeling, image fu-
sion, shape analysis, to medical image analysis. The process finds
the spatial correspondences between different images that may be
taken at different time or by modalities of acquisition. Recently, it
has been shown that incorporating prior knowledge into the regis-
tration process has the potential to significantly improvethe image
registration results. Therefore, many researchers have been putting
lotsofeffortin thisfield.
Inthisthesis,weinvestigatethepossibilityofimprovingtherobust-
ness and accuracy of image registration, by incorporating anatom-
ical and appearance priors. We explored and formulated several
methodstoincorporateanatomicalandappearancepriorknowledge
intoimageregistrationprocess explicitlyand implicitly.
To incorporate the anatomical prior, we propose to utilize the seg-
mentation information that is readily available. An intensity-based
similarity measure named structural encoded mutual information
is introduced by emphasizing the structural information. Then we
useregistrationoftheanatomical-meaningfulpointsetsthatareex-
tracted from thesurface/contourofthesegmentationto generatean
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anatomical meaningful deformation field. The two types of data-
driven prior information are then combined in a hybrid manner to
jointly guide the image registration process. The proposed method
is fully validated in a pre-operative CT and non-contrast-enhanced
C-arm CT registration framework for Trans-catheter Aortic Valve
Implantation(TAVI)and otherapplications.
To incorporate the appearance prior, we proposed to describe the
intensitymatchinginformationby usingnormalized pointwisemu-
tualinformationwhichcanbelearntfromthetrainingsamples. The
intensity matching information is then incorporated into the image
registration framework by introducing two novel similarity mea-
sures, namely, weighted mutual information and weighted entropy.
Theproposedsimilaritymeasureshavedemonstratedtheirwideap-
plicabilityranging from natural imageexamplesto medical images
fromdifferent applicationsand modalities.
Lastly, we explored the feasibility of generating different image
modalities from one source image based on prior image matching
knowledgethatisextractedfromthedatabase. Thesynthesizedim-
agesbasedonpriorknowledgecanbethenusedforimageregistra-
tion. Using the synthesized images as the intermediate step in the
multi-modality registration process explicitly simplifies the prob-
lemto asinglemodalityimageregistrationproblem.
Themethodsandtechniquesweproposedinthisthesiscanbecom-
bined and/or tailored for any specific applications. We believe that
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