—— Image Display, Enhancement, and Analysis (IDEA) Group

UNC IDEA group consists of the IDEA Lab in the Department of Radiology and the Image Analysis Core Lab in the Biomedical Research Imaging Center (BRIC). The IDEA lab is devoted to the development of novel image analysis methods and tools, and their applications to various clinical research and trials. The developed methods include deformable registration (HAMMER), deformable segmentation (AFDM), and multivariate pattern classification algorithms. These methods have been applied to various studies on brain diseases and development (including MCI, AD, Schizophrenia, and Neonate Development Study), heart, breast cancer, and prostate cancer. The image analysis core in BRIC supports the image storage and analysis needs of scientists in UNC. It also provides services for brain structural and functional analysis, small animal imaging analysis, visualization, and others.

Follow our new articles automatically by clicking Follow new articles in this web and providing your email address.

New Papers:

  1. “Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Accelerated Data in Magnetic Resonance Fingerprinting”, IEEE Transactions on Medical Imaging, 2019. [Zhenghan Fang, Yong Chen, Mingxia Liu, Lei Xiang, Qian Zhang, Qian Wang, Weili Lin, Dinggang Shen]
  2. “Hierarchical Rough-to-Fine Model for Infant Age Prediction based on Cortical Features”, IEEE Journal of Biomedical and Health Informatics, 2019. [Dan Hu, Zhengwang Wu, Weili Lin, Gang Li, Dinggang Shen]
  3. “Multi-task Exclusive Relationship Learning for Alzheimer’s Disease Progression Prediction with Longitudinal Data”, Medical Image Analysis, 2019. [Mingliang Wang, Daoqiang Zhang, Dinggang Shen, Mingxia Liu]
  4. “Local diffusion homogeneity provides supplementary information in T2DM-related WM microstructural abnormality detection”, Frontiers in Neuroscience, 2019. [Yi Liang, Han Zhang, Xin Tan, Jiarui Liu, Chunhong Qin, Hui Zeng, Yanting Zheng, Yujie Liu, Jingxian Chen, Xi Leng, Shijun Qiu, Dinggang Shen]
  5. “Multi-Channel Framelet Denoising of Diffusion-Weighted Images”, PLOS ONE, 2019. [Geng Chen#, Jian Zhang#, Yong Zhang, Bin Dong, Dinggang Shen, Pew-Thian Yap] #Co-first authors
  6. “Noise Reduction in Diffusion MRI Using Non-Local Self-Similar Information in Joint x-q Space”, Medical Image Analysis, 2019. [Geng Chen, Yafeng Wu, Dinggang Shen, Pew-Thian Yap]
  7. “Multi-Site Harmonization of Diffusion MRI Data via Method of Moments”, IEEE Transactions on Medical Imaging, 2019. [Khoi Minh Huynh, Geng Chen, Ye Wu, Dinggang Shen, and Pew-Thian Yap]
  8. “Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification”, Pattern Recognition, 2019. [Renping Yu, Lishan Qiao, Mingming Chen, Seong-Whan Lee, Xuan Fei, Dinggang Shen]
  9. “Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment”,  Neuroinformatics, 2018. [Han Zhang, Panteleimon Giannakopoulos, Sven Haller, Dinggang Shen*]
  10. “Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. [Chunfeng Lian, Mingxia Liu, Jun Zhang, Dinggang Shen]
  11. “Strength and Similarity Guided Group-level Brain Functional Network Construction for MCI Diagnosis”, Pattern Recognition, 2018. [Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Seong-Whan Lee, Dinggang Shen]
  12. “A New Image Similarity Metric for Improving Deformation Consistency in Graph Based Groupwise Image Registration”,IEEE Transactions on Biomedical Engineering, 2018. [Zhenyu Tang, Pew-Thian Yap, , Dinggang Shen]