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dc.contributor.authorZheng, Chaojie
dc.date.accessioned2019-08-23
dc.date.available2019-08-23
dc.date.issued2019-01-25
dc.identifier.urihttp://hdl.handle.net/2123/20948
dc.description.abstractThe popularization of information-sensing devices and rapid development of data storage and computing capability have launched the ongoing data explosion, and the value of such information assets has been widely acknowledged. Data often come in high volume, variety and velocity, and people are striving to mine useful information and new knowledge from the growing data sets for various domain applications. The fusion of heterogeneous types of data becomes increasingly essential for analysis and decision making in data incentive industry, especially in healthcare industry. Image registration, as a branch of data fusion technique, is the process of determining the geometric transformation that relates correspondences in images. As the image registration result is often used as the input of further analysis or process, it is crucial to establish the image correspondence through an accurate and confident registration method. However, due to the diverse characteristics of images, there are inherent uncertainty issues associated with various aspects of the registration process. The source of such uncertainty issues could be divided into two categories: input images and registration algorithms. This thesis provides three registration methods with the consideration of uncertainty issues the of registration processes. Our major contributions include: 1. A topology-guided deformable registration (TDR) framework to deal with the image correspondence uncertainty issue in the derivation of deformation direction. 2. A novel laminar flow (LF) model with an analogy to laminar flow regime from fluid dynamics to simulate the derivation of registration transformation. 3. A multi-view collaborative learning based image registration (MVCIR) framework to tackle the uncertainty of deciding reliable feature space for the correspondence inference.en_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectimage registrationen_AU
dc.subjectdeformable registrationen_AU
dc.subjectregistration uncertaintyen_AU
dc.subjectdemonsen_AU
dc.subjectlaminar flowen_AU
dc.subjectmultiview collaborative learningen_AU
dc.titleDeformable Image Registration with Uncertainty-Awarenessen_AU
dc.typeThesisen_AU
dc.type.thesisDoctor of Philosophyen_AU
usyd.facultyFaculty of Engineering, School of Computer Scienceen_AU
usyd.degreeDoctor of Philosophy Ph.D.en_AU
usyd.awardinginstThe University of Sydneyen_AU


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