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Serag, A., Aljabar, P., Ball, G., Counsell, S.J., Boardman, J.P., Rutherford, M.A., Edwards, A.D., Hajnal, J.V. and Rueckert, D. (2012) Construction of a Consistent High-Definition Spatio-Temporal Atlas of the Developing Brain Using Adaptive Kernel Regression. NeuroImage, 59, 2255-2265.
https://doi.org/10.1016/j.neuroimage.2011.09.062

has been cited by the following article:

• TITLE: Accelerated Generation of Spatiotemporal Atlas through Fast Template Matching

  AUTHORS: Lianjun Zhang, Lifang Zhao, Gang Liu

  KEYWORDS: Spatiotemporal Atlas, Template Matching, Classification

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.1, January 6, 2020

  ABSTRACT: A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging. However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved. This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem. In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity. The performance has been systematically evaluated on public accessible data sets. The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.