By Chenhui Hu, Lin Cheng, Jorge Sepulcre, Georges El Fakhri, Yue M. Lu, Quanzheng Li (auth.), James C. Gee, Sarang Joshi, Kilian M. Pohl, William M. Wells, Lilla Zöllei (eds.)
This publication constitutes the complaints of the twenty third overseas convention on details Processing in clinical Imaging, IPMI 2013, held in Asilomar in June/July 2013. The 26 complete papers and 38 poster papers provided during this quantity have been rigorously reviewed and chosen from 199 submissions. The papers are equipped in topical sections on connectivity, groupwise registration, neuro segmentation, statistical research, dynamic imaging, cortical floor registration, diffusion MRI, practical imaging, torso photo research, and tract analysis.
Read Online or Download Information Processing in Medical Imaging: 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28–July 3, 2013. Proceedings PDF
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Additional info for Information Processing in Medical Imaging: 23rd International Conference, IPMI 2013, Asilomar, CA, USA, June 28–July 3, 2013. Proceedings
The last row illustrates the projection energy distributions of a signal on the two sets of eigenvectors of the graph Laplacians. 2 0 5 10 15 20 25 (a) NC group 30 35 40 0 0 5 10 15 20 25 30 (b) AD group Fig. 4. Projection errors in the leave-one-out tests when the true data is from either NC or AD group. Note that the x-axis is the index of subjects. Matched Signal Detection on Graphs 5 11 Conclusion In this paper, we formulate the MSD for graph-structure data under diﬀerent signal models. We consider the signals that are either smooth on the graph or randomly generated from a known prior distribution.
To quantitatively measure the robustness of proposed sampling method, we define the measure of consistence (MOC) as follows: MOC=∑ , (5) 20 D. Zhu et al. where and are two regression matrices; i and j are the indices of row and column. We calculated the MOC between any two rounds and the result is shown in Fig. 5(b). 08, which is very small considering the information loss during the regression process. 5 for other subjects, suggesting the robustness and reproducibility of the SW-LASSO model. Fig.
Identifying Graph-Structured Activation Patterns in Networks. In: Proc. of Neural. Info. Proc. Sys. (2010) 16. : Probability on Graphs: Random Processes on Graphs and Lattices, vol. 1. Cambridge University Press (2010) 17. : Compressive Matched Subspace Detection. In: Proc. , pp. 120–124 (2009) 18. : Small-World Brain Networks. The Neuroscientist 12, 512–523 (2006) 19. : Collective Dynamics of “Small-World” Networks. Nature 2, 393–440 (1998) 20. : Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease.