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Automated Calculation Of Bifurcation Carotid Angle For Analyzing The Risk Of Carotis Plaques By Using Carotid Ct Angiographic Images

机译:分叉颈动脉角度的自动计算,用于通过使用Ct血管造影图像分析颈动脉斑块的风险

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The aim of this study is calculation of bifurcation carotid angle by detection of vessel boundaries to assist the medical doctors if this angle is a risk factor about formation of carotid plaques.Carotid ct angiography images are clustered automatically by ISODATA unsupervised classification algorithm. Since the spectral digital numbers (DN) of vessel pixels are bigger than the other part of the images, the cluster which has the biggest median value of DN among all other classes gives the vessel class. The cluster image in raster format is converted into the vector format which allows working on the vessel geometry. The converted vector vessel cluster dataset has been simplified using Douglas-Peucker algorithm to eliminate the zigzag effects of pixel data which are remained on the vector form dataset. Then the cluster polygon is converted to lines and the vertices which will be used for the calculation of bifurcation carotid angle. For sorting the vertex points to calculate the angle on each vertex, alpha-shapes algorithm is applied along the boundary. Then all the angles on each vertex point along the boundary of vessels are calculated. It is also visually clear that the angle which has the minimum value among all the calculated angles, gives the bifurcation carotid angle for one projected plane. The final carotid angle has calculated and 18 sample datasets are used to test the method.
机译:这项研究的目的是通过检测血管边界来计算分叉的颈动脉角度,如果该角度是形成颈动脉斑块的危险因素,则可以帮助医生。颈动脉CT血管造影图像是通过ISODATA无监督分类算法自动聚类的。由于血管像素的光谱数字数(DN)大于图像的其他部分,因此在所有其他类别中,DN的中值最大的群集为血管类别。栅格格式的聚类图像将转换为矢量格式,从而可以处理容器的几何形状。使用Douglas-Peucker算法简化了转换后的矢量血管簇数据集,以消除残留在矢量形式数据集上的像素数据的锯齿形影响。然后将簇多边形转换为线和顶点,这些线和顶点将用于计算分叉颈动脉角度。为了对顶点进行排序以计算每个顶点上的角度,沿边界应用了alpha形状算法。然后,计算沿血管边界的每个顶点上的所有角度。从视觉上也很清楚,在所有计算出的角度中具有最小值的角度给出了一个投影平面的分叉颈动脉角度。已计算出最终的颈动脉角度,并使用18个样本数据集来测试该方法。

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