UNC IDEA Group

------ 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.

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New Papers:

  1. "Embarrassingly Parallel Acceleration of Global Tractography via Dynamic Domain Partitioning”, Frontiers in Neuroinformatics, 2016. [Haiyong Wu, Geng Chen, Yan Jin, Dinggang Shen*, Pew-Thian Yap*] *Co-corresponding authors
  2. "Disrupted Functional Connectome in Antisocial Personality Disorder", Brain Imaging and Behavior, 2016. [Weixiong Jiang, Feng Shi, Jian Liao, Huasheng Liu, Tao Wang, Celina Shen, Hui Shen, Dewen Hu, Wei Wang, Dinggang Shen]
  3. "Topographic Information based High-Order Functional Connectivity and its Application in Abnormality Detection for Mild Cognitive Impairment”, Journal of Alzheimer's Disease, 2016. [Han Zhang, Xiaobo Chen, Feng Shi, Gang Li, Min-jeong Kim, Panteleimon Giannakopoulos, Sven Haller, Dinggang Shen]
  4. "Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains”, IEEE Transactions on Medical Imaging, 2016. [Yuyao Zhang, Feng Shi, Guorong Wu, Li Wang, Pew-Thian Yap, Dinggang Shen]
  5. "Multi-Tissue Decomposition of Diffusion MRI Signals via l0 Sparse-Group Estimation”, IEEE Transactions on Image Processing, 2016. [Pew-Thian Yap, Yong Zhang, Dinggang Shen]