Document Type


Date of Degree

Spring 2017

Access Restrictions


Degree Name

PhD (Doctor of Philosophy)

Degree In

Electrical and Computer Engineering

First Advisor

Christensen, Gary E.

First Committee Member

Durumeric, Oguz C.

Second Committee Member

Johnson, Hans J.

Third Committee Member

Kuhl, Jon G.

Fourth Committee Member

Saha, Punam K.


In the field of medical imaging, image registration methods are useful for many applications such as inter- and intra-subject morphological comparisons, creation of population atlases, delivery of precision therapies, etc. A user may want to know which is the most suitable registration algorithm that would work best for the intended application, but the vastness of medical image registration applications makes evaluation and comparison of image registration performance a non-trivial task. In general, evaluating image registration performance is not straightforward because in most image registration applications there is an absence of “Gold Standard” or ground truth correspondence map to compare against. It is therefore the primary goal of this thesis work to provide a means for recommending the most appropriate registration algorithm for a given task. One of the contributions of this thesis is to examine image registration algorithm performance at the component level. Another contribution of this thesis is to catalog the benefits and limitations of many of the most commonly used image registration evaluation approaches. One incremental contribution of this thesis was to demonstrate how existing evaluation methods can be applied in the midpoint coordinate system to evaluate some symmetric image registration algorithms such as the SyN registration algorithm. Finally, a major contribution of this thesis was to develop tools to evaluate and visualize 2D and 3D image registration shape collapse. This thesis demonstrates that many current diffeomorphic image registration algorithms suffer from the collapse problem, provides the first visualizations of the collapse problem in 3D for simple shapes and real human brain MR images, and provides the first experiments that demonstrate how adjusting image registration parameters can mitigate the collapse problem to some extent.

Public Abstract

From ultrasound exams that an expectant mothers get before birth, to routine CT/MRI screenings during the course of our lives, medical imaging is an integral part of human life. Medical imaging has advanced to the point of not only visualizing human anatomy, but also being able to provide information for diagnosis and therapy. Image registration is a key component in medical imaging that allows mapping of anatomical structures between images. In this work, an assessment of current image registration techniques is made, and novel approaches for evaluating registration performance are introduced. The contributions made in this work will help improve image registration accuracy, which in turn will ultimately advance the research in diagnosis and therapy of diseases.


Evaluation, Image Registration, Image Registration Evaluation, Medical Imaging, Registration, Validation


xvi, 147 pages


Includes bibliographical references (pages 140-147).


Copyright © 2017 Joo Hyun Song