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.

翻译：本文提出了一种基于像素分类的方法，用于X射线血管造影中的血管分割。该方法利用纹理特征，如各向异性扩散、基于Hessian矩阵的特征、数学形态学和统计量。这些特征从每个像素的邻域中提取。该方法还采用了ELEMENT方法论，即创建由区域生长控制的像素分类，其中分类结果会影响后续像素的分类。使用随机森林分类器预测像素是否属于血管结构。该方法在文献中取得了最佳准确率（95.48%），优于无监督的最先进方法。