一种用于分割磁共振血管造影（MRA）图像的新型自适应血管概率模型。
A new adaptive probabilistic model of blood vessels for segmenting MRA images.
作者信息
El-Baz Ayman, Farag Aly A, Gimel'farb Georgy, El-Ghar Mohamed A, Eldiasty Tarek
机构信息
Computer Vision and Image Processing Laboratory, University of Louisville, Louisville, KY 40292, USA.
出版信息
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):799-806. doi: 10.1007/11866763_98.
A new physically justified adaptive probabilistic model of blood vessels on magnetic resonance angiography (MRA) images is proposed. The model accounts for both laminar (for normal subjects) and turbulent blood flow (in abnormal cases like anemia or stenosis) and results in a fast algorithm for extracting a 3D cerebrovascular system from the MRA data. Experiments with synthetic and 50 real data sets confirm the high accuracy of the proposed approach.
提出了一种新的基于物理原理的磁共振血管造影（MRA）图像上血管的自适应概率模型。该模型考虑了层流（针对正常受试者）和湍流（在贫血或狭窄等异常情况下），并产生了一种从MRA数据中提取三维脑血管系统的快速算法。对合成数据集和50个真实数据集的实验证实了所提方法的高精度。
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