------ 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. "Automated Segmentation of Dental CBCT Image with Prior-guided Sequential Random Forests”, Medical Physics, 2015. [Li Wang, Yaozong Gao, Feng Shi, Gang Li, Ken-Chung Chen, Zhen Tan, James J. Xia, Dinggang Shen]
  2. "Cortical asymmetries in unaffected siblings of patients with obsessive-compulsive disorder”, Psychiatry Research: Neuroimaging, 2015. [Ziwen Peng, Gang Li, Feng Shi, Changzheng Shi, Qiong Yang, Raymond C.K. Chan, Dinggang Shen]
  3. "Graph Guided Joint Prediction of Class Label and Clinical Scores for the Alzheimer's Disease”, Brain Structure and Function, 2015. [Guan Yu, Yufeng Liu, Dinggang Shen]
  4. "Building Dynamic Population Graph for Accurate Correspondence Detection”, Medical Image Analysis, 2015. [Shaoyi Du, Yanrong Guo, Gerard Sanroma, Dong Ni, Guorong Wu, Dinggang Shen]
  5. "Spatiotemporal Patterns of Cortical Fiber Density in Developing Infants, and Their Relationship with Cortical Thickness”, Human Brain Mapping, 2015. [Gang Li, Tianming Liu, Dong Ni, Weili Lin, John H. Gilmore, Dinggang Shen]