Extraction of various perfusion components from dynamic-susceptibility- contrast (DSC) MR brain images is critical for the analysis of brain perfusion. According to the variation of temporal signal on different brain tissues, one can segment whole brain area into distinct blood supply patterns which are vital for the profound analysis of cerebral hemodynamics. In this study, independent component analysis (ICA) is used to project the perfusion image data into independent components from which each elucidated tissue cluster can be automatically segment out by using the hierarchical clustering (HC). Five normal subjects and a case of internal carotid artery stenosis subjects were analyzed. The results demonstrated that ICA-HC is effective in multi-tissue hemodynamic classification which improves differentiation of pathological and physiological hemodynamics.
|主出版物標題||29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07|
|出版狀態||已發佈 - 2007|
|事件||29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, 法国|
持續時間: 8月 23 2007 → 8月 26 2007
|會議||29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07|
|期間||8/23/07 → 8/26/07|
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