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.

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

 

New Papers:

  1. "Multimodal Manifold-Regularized Transfer Learning for MCI Conversion Prediction”, accepted for Brain Imaging and Behavior, 2015. [Bo Cheng, Mingxia Liu, Heung-Il Suk, Dinggang Shen*, Daoqiang Zhang*]  *Co-corresponding authors.
  2. "View-Centralized Multi-Atlas Classification for Alzheimer’s Disease Diagnosis”, accepted for Human Brain Mapping, 2015. [Mingxia Liu, Daoqiang Zhang, Dinggang Shen]
  3. "A Learning-Based Prostate Segmentation Method via Joint Transductive Feature Selection and Regression”, accepted for Neurocomputing, 2015. [Yinghuan Shi, Yaozong Gao, Shu Liao, Daoqiang Zhang, Yang Gao, Dinggang Shen]
  4. "MRI-based Intelligence Quotient (IQ) Estimation with Sparse Learning”, accepted for PLOS ONE, 2015. [Liye Wang, Chong-Yaw Wee, Heung-Il Suk, Xiaoying Tang, Dinggang Shen
  5. "LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images”, accepted for Neuroimage, 2014. [Li Wang, Yaozong Gao, Feng Shi, Gang Li, John H. Gilmore, Weili Lin, Dinggang Shen]