P
Pew Thian Yap
Researcher at University of North Carolina at Chapel Hill
Publications - 351
Citations - 9707
Pew Thian Yap is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Computer science & Image registration. The author has an hindex of 42, co-authored 316 publications receiving 7452 citations. Previous affiliations of Pew Thian Yap include Stanford University & University of Malaya.
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Image analysis by Krawtchouk moments
TL;DR: It is shown that the Krawtchouk moments can be employed to extract local features of an image, unlike other orthogonal moments, which generally capture the global features.
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Infant brain atlases from neonates to 1- and 2-year-olds.
TL;DR: It is expected that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains.
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Identification of MCI individuals using structural and functional connectivity networks
Chong Yaw Wee,Pew Thian Yap,Daoqiang Zhang,Kevin Denny,Jeffrey N. Browndyke,Guy G. Potter,Kathleen A. Welsh-Bohmer,Lihong Wang,Dinggang Shen +8 more
TL;DR: This study attempts to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance and indicates that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently.
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Two-Dimensional Polar Harmonic Transforms for Invariant Image Representation
TL;DR: A set of 2D transforms, based on a set of orthogonal projection bases, to generate aSet of features which are invariant to rotation, called Polar Harmonic Transforms (PHTs), which encompass the orthogonality and invariance advantages of Zernike and pseudo-Zernike moments, but are free from their inherent limitations.
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LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations
TL;DR: Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed image SR method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolations, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.