用机器学习与区域生长结合，精准分割心电造影中的血管结构。
X-Ray cardiac angiographic vessel segmentation based on pixel classification using machine learning and region growing
- 基于纹理特征和随机森林分类器进行像素级血管判断
- 在公开数据集上达到95.48%的分割准确率，领先现有方法
- 适合医学影像分析、心血管疾病辅助诊断的研究者参考
本文提出一种基于像素分类的心脏血管造影图像血管分割方法。该方法提取每个像素邻域的纹理特征，包括各向异性扩散、海森矩阵特征、数学形态学及统计特征。同时采用ELEMENT方法，通过区域生长控制像素分类过程，分类结果影响后续像素判断。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最高准确率（95.48%），显著优于无监督的最新方法。
原文摘要 · Abstract (English)
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.
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