کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6023337 | 1580874 | 2016 | 10 صفحه PDF | دانلود رایگان |

In this paper, we introduce a new locally multivariate procedure to quantitatively extract voxel-wise patterns of abnormal perfusion in individual patients. This a contrario approach uses a multivariate metric from the computer vision community that is suitable to detect abnormalities even in the presence of closeby hypo- and hyper-perfusions. This method takes into account local information without applying Gaussian smoothing to the data. Furthermore, to improve on the standard a contrario approach, which assumes white noise, we introduce an updated a contrario approach that takes into account the spatial coherency of the noise in the probability estimation.Validation is undertaken on a dataset of 25 patients diagnosed with brain tumours and 61 healthy volunteers. We show how the a contrario approach outperforms the massively univariate general linear model usually employed for this type of analysis.
Journal: NeuroImage - Volume 134, 1 July 2016, Pages 424-433