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|Title:||MULTISCALE KERNELS FOR DIFFEOMORPHIC BRAIN IMAGE AND SURFACE MATCHING||Authors:||TAN MING ZHEN||Keywords:||Image Processing, Brain Registration, Multiscale Kernels||Issue Date:||20-Jan-2017||Citation:||TAN MING ZHEN (2017-01-20). MULTISCALE KERNELS FOR DIFFEOMORPHIC BRAIN IMAGE AND SURFACE MATCHING. ScholarBank@NUS Repository.||Abstract:||This thesis focuses on the multiscale based improvements of the large deformation diffeomorphic metric mapping (LDDMM) framework, with specific emphasis on human brain registration. Motivated by the concept of multiscale Gaussian reproducing kernels and their proven efficacy in mapping objects of multiple scales of variation, we discuss two novel multiscale implementations for the LDDMM framework, with applications in both surface and volume-based matching functionals. In the first method, we demonstrated marked improvements in accuracy and computational costs via a coarse-to-fine multiresolution surface registration framework that utilises a mixture of weighted Gaussian kernels. The method incorporates sparsity without the use of augmented sparse-penalty functions and was also shown to alleviate the undesired in-folding effects of the popular currents surface matching functional. In the second method, we proposed the use of frame-based reproducing kernels that construct hierarchical multiscale structures based on the multiresolution analysis framework for wavelets. These reproducing kernels are easy to tune and demonstrate improved accuracy for image-based registrations involving multiple scales of variation. Importantly, these frame-based kernels were shown to be feasible for practical integrated whole-brain registration frameworks involving anatomical landmarks, manually-delineated curves and surface representations alongside volume-based brain mapping.||URI:||http://scholarbank.nus.edu.sg/handle/10635/136511|
|Appears in Collections:||Ph.D Theses (Open)|
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