Implementation of visual data mining for unsteady blood flow field in an aortic aneurysmReport as inadecuate




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Journal of Visualization

, Volume 14, Issue 4, pp 393–398

First Online: 27 August 2011Received: 16 June 2010Accepted: 10 July 2011

AbstractThis study was performed to determine the relations between the features of wall shear stress and aneurysm rupture. For this purpose, visual data mining was performed in unsteady blood flow simulation data for an aortic aneurysm. The time-series data of wall shear stress given at each grid point were converted to spatial and temporal indices, and the grid points were sorted using a self-organizing map based on the similarity of these indices. Next, the results of cluster analysis were mapped onto the real space of the aortic aneurysm to specify the regions that may lead to aneurysm rupture. With reference to previous reports regarding aneurysm rupture, the visual data mining suggested specific hemodynamic features that cause aneurysm rupture.

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AbstractOpen image in new windowKeywordsVisual data mining Self-organizing map Aortic aneurysm Wall shear stress An erratum to this article can be found at http:-dx.doi.org-10.1007-s12650-011-0111-0

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Author: Seiichiro Morizawa - Koji Shimoyama - Shigeru Obayashi - Kenichi Funamoto - Toshiyuki Hayase

Source: https://link.springer.com/







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