------ 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 Auto-context Convolutional Neural Networks for Standard-Dose PET Image Estimation from Low-Dose PET/MRI”, Neurocomputing, 2017. [Lei Xiang, Yu Qiao, Dong Nie, Le An, Weili Lin, Qian Wang, Dinggang Shen]
  2. "Detecting Anatomical Landmarks from Limited Medical Imaging Data using Two-Stage Task-Oriented Deep Neural Networks”, IEEE Transactions on Image Processing, 2017. [Jun Zhang, Mingxia Liu, Dinggang Shen]
  3. "Extraction of Dynamic Functional Connectivity from Brain Grey Matter and White Matter for MCI Classification”, Human Brain Mapping, 2017. [Xiaobo Chen, Han Zhang, Lichi Zhang, Celina Shen, Seong-Whan Lee, Dinggang Shen]
  4. "Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis”, Scientific Reports, 2017. [Yu Zhang, Han Zhang, Xiaobo Chen, Seong-Whan Lee, Dinggang Shen]
  5. "Alzheimer’s Disease Diagnosis using Landmark-based Features from Longitudinal Structural MR Images”, IEEE Journal of Biomedical and Health Informatics, 2017. [Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen]