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. "Identification of Infants at High-Risk for Autism Spectrum Disorder Using Multi-Parameter Multi-Scale White Matter Connectivity Networks”, Human Brain Mapping, 2015. [Yan Jin, Chong-Yaw Wee, Feng Shi, Kim-Han Thung, Dong Ni, Pew-Thian Yap, Dinggang Shen]
  2. "Subspace Regularized Sparse Multi-Task Learning for Multi-Class Neurodegenerative Disease Identification”, IEEE Transactions on Biomedical Engineering, 2015. [Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, Dinggang Shen]
  3. "Estimating Patient-Specific and Anatomically-Correct Reference Model for Craniomaxillofacial Deformity via Sparse Representation”, Medical Physics, 2015. [Li Wang, Yi Ren, Yaozong Gao, Zhen Tang, Ken-Chung Chen, Jiangu Li, Steve GF Shen, Jin Yan, Philip K.M. Lee, Ben Chow, James J. Xia, Dinggang Shen]
  4. "Prediction of standard-dose brain PET image by using MR and low-dose brain [18F]FDG PET images”, Medical Physics, 2015. [Jiayin Kang, Yaozong Gao, Feng Shi, David S. Lalush, Weili Lin, Dinggang Shen]
  5. "Estimating CT Image from MRI Data Using Structured Random Forest and Auto-context Model”, IEEE Trans. On Medical Imaging, 2015. [Tri Huynh, Yaozong Gao, Jiayin Kang, Li Wang, Pei Zhang, Jun Lian, Dinggang Shen]