Browsing by Subject "Image Interpretation, Computer-Assisted"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Adaptive-optics Optical Coherence Tomography Processing Using a Graphics Processing Unit
    (Institute of Electrical and Electronics Engineers, 2014) Shafer, Brandon A.; Kriske, Jeffery E.; Kocaoglu, Omer P.; Turner, Timothy L.; Liu, Zhuolin; Lee, John J.; Miller, Donald T.; Department of Engineering Technology, School of Engineering and Technology
    Graphics processing units are increasingly being used for scientific computing for their powerful parallel processing abilities, and moderate price compared to super computers and computing grids. In this paper we have used a general purpose graphics processing unit to process adaptive-optics optical coherence tomography (AOOCT) images in real time. Increasing the processing speed of AOOCT is an essential step in moving the super high resolution technology closer to clinical viability.
  • Thumbnail Image
    Item
    Human connectome module pattern detection using a new multi-graph MinMax cut model
    (Springer, 2014) Wang, De; Wang, Yang; Nie, Feiping; Cai, Weidong; Saykin, Andrew J.; Shen, Li; Huang, Heng; Department of Radiology and Imaging Sciences, IU School of Medicine
    Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.
  • Thumbnail Image
    Item
    Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach
    (Springer, 2017-06) Du, Lei; Zhang, Tuo; Liu, Kefei; Yan, Jingwen; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Han, Junwei; Guo, Lei; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.