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. "Joint Feature-Sample Selection and Robust Diagnosis of Parkinson's Disease from MRI data”, NeuroImage, 2016. [Ehsan Adeli, Feng Shi, Le An, Chong-Yaw Wee, Guorong Wu, Tao Wang, Dinggang Shen]
  2. “Scalable Joint Segmentation and Registration Framework for Infant Brain Images”, Neurocomputing, 2016. [Pei Dong, Li Wang, Weili Lin, Dinggang Shen*, Guorong Wu*] * Co-corresponding authors
  3. "Semi-Supervised Tripled Dictionary Learning for Standard-dose PET Image Prediction using Low-dose PET and Multimodal MRI”, IEEE Transactions on Biomedical Engineering, 2016. [Yan Wang, Guangkai Ma, Le An, Feng Shi, Pei Zhang, David S. Lalush, Xi Wu, Jiliu Zhou, Dinggang Shen]
  4. "Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment”, Frontiers in Aging Neuroscience, 2016. [Tao Wang, Feng Shi, Yan Jin, Weixiong Jiang, Dinggang Shen*, Shifu Xiao*] * Corresponding authors
  5. "Dynamic Tree-Based Image Registration under Large Deformation for Multi-Atlas Segmentation”, Computerized Medical Imaging and Graphics, 2016. [Pei Zhang, Guorong Wu, Yaozong Gao, Pew-Thian Yap, Dinggang Shen]