J
Jiang Liu
Researcher at Southern University of Science and Technology
Publications - 398
Citations - 11842
Jiang Liu is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Image segmentation & Optic cup (anatomical). The author has an hindex of 40, co-authored 367 publications receiving 7564 citations. Previous affiliations of Jiang Liu include Institute for Infocomm Research Singapore & National University of Singapore.
Papers
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Journal ArticleDOI
Nanomaterial Probes for Nuclear Imaging
Vanessa Jing Xin Phua,Chang-Tong Yang,Bin Xia,Sean X. Yan,Jiang Liu,Swee Eng Aw,Tao He,David Chee Eng Ng +7 more
TL;DR: New developments in nanomaterials are expected to introduce a paradigm shift in nuclear imaging, thereby creating new opportunities for theranostic medical imaging tools.
Proceedings ArticleDOI
Cell Clumping Quantification and Automatic Area Classification in Peripheral Blood Smear Images
Wei Xiong,Sim Heng Ong,Christina Yong Xin Kang,Joo-Hwee Lim,Jiang Liu,Daniel Racoceanu,Kelvin Weng Chiong Foong +6 more
TL;DR: This work has validated the method over 4500 testing cell images and achieved 89% sensitivity and 87% specificity, and measured the goodness of such areas in terms of the degree of cell spread and thedegree of clumping.
Book ChapterDOI
A Robust Outlier Elimination Approach for Multimodal Retina Image Registration
Ee Ping Ong,Jimmy Lee,Jun Cheng,Guozhen Xu,Beng Hai Lee,Augustinus Laude,Stephen C. Teoh,Tock Han Lim,Damon Wing Kee Wong,Jiang Liu +9 more
TL;DR: This paper's experiments on registration of fundus-fluorescein angiographic image pairs show that the proposed RSW-LTS approach significantly outperforms the Harris-PIIFD scheme and also shows that the approach outperforms other outlier elimination approaches such as RANSAC and MSAC.
Proceedings ArticleDOI
Automatic atherosclerotic heart disease detection in intracoronary optical coherence tomography images
TL;DR: An automatic atherosclerotic disease detection system on intracoronary OCT images is presented and four-fold cross-validation process is conducted to evaluate the proposed system.
Journal ArticleDOI
Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning
Ting Zhou,Jianlong Yang,Kang Zhou,Liyang Fang,Yan Hu,Jun Cheng,Yitian Zhao,Xiangping Chen,Shenghua Gao,Jiang Liu +9 more
TL;DR: In this article, a cycle-consistent adversarial network architecture was employed to convert the centrally cropped 3'×'3 mm2 field of view (FOV) of OCTA images to the native 3 '×' 3 mm2en face OCTA image, which has a sampling density of 12.2 µm.