This work proposes a pixel-classification approach for vessel segmentation in x-ray angiograms. The proposal uses textural features such as anisotropic diffusion, features based on the Hessian matrix, mathematical morphology and statistics. These features are extracted from the neighborhood of each pixel. The approach also uses the ELEMENT methodology, which consists of creating a pixel-classification controlled by region-growing where the result of the classification affects further classifications of pixels. The Random Forests classifier is used to predict whether the pixel belongs to the vessel structure. The approach achieved the best accuracy in the literature (95.48%) outperforming unsupervised state-of-the-art approaches.

这项工作提出了一种用于 X 射线血管造影中血管分割的像素分类方法。该提案使用了纹理特征，例如各向异性扩散、基于 Hessian 矩阵的特征、数学形态学和统计学。这些特征是从每个像素的邻域中提取的。该方法还使用 ELEMENT 方法，该方法包括创建由区域增长控制的像素分类，其中分类结果会影响像素的进一步分类。随机森林分类器用于预测像素是否属于血管结构。该方法在文献中取得了最好的准确度（95.48%），优于无监督的最先进方法。