The retinal vessel is one of the determining factors in an ophthalmic examination. Automatic extraction of retinal vessels from low-quality retinal images still remains a challenging problem. In this paper, we propose a robust and effective approach that qualitatively improves the detection of low-contrast and narrow vessels. Rather than using the pixel grid, we use a superpixel as the elementary unit of our vessel segmentation scheme. We regularize this scheme by combining the geometrical structure, texture, color, and space information in the superpixel graph. And the segmentation results are then refined by employing the efficient minimum spanning superpixel tree to detect and capture both global and local structure of the retinal images. Such an effective and structure-aware tree detector significantly improves the detection around the pathologic area. Experimental results have shown that the proposed technique achieves advantageous connectivity-area-length (CAL) scores of 80.92% and 69.06% on two public datasets, namely, DRIVE and STARE, thereby outperforming state-of-the-art segmentation methods. In addition, the tests on the challenging retinal image database have further demonstrated the effectiveness of our method. Our approach achieves satisfactory segmentation performance in comparison with state-of-the-art methods. Our technique provides an automated method for effectively extracting the vessel from fundus images.

中文翻译：

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使用最小跨度超像素树检测器进行视网膜血管分割


视网膜血管是眼科检查的决定因素之一。从低质量视网膜图像中自动提取视网膜血管仍然是一个具有挑战性的问题。在本文中，我们提出了一种稳健有效的方法，可以定性地改进低对比度和狭窄血管的检测。我们不使用像素网格，而是使用超像素作为血管分割方案的基本单位。我们通过结合超像素图中的几何结构、纹理、颜色和空间信息来规范该方案。然后通过采用有效的最小跨度超像素树来检测和捕获视网膜图像的全局和局部结构来细化分割结果。这种有效且具有结构感知能力的树木检测器显着改善了病理区域周围的检测。实验结果表明，该技术在两个公共数据集 DRIVE 和 STARE 上实现了 80.92% 和 69.06% 的连通区域长度（CAL）得分，从而优于最先进的分割方法。此外，在具有挑战性的视网膜图像数据库上的测试进一步证明了我们方法的有效性。与最先进的方法相比，我们的方法实现了令人满意的分割性能。我们的技术提供了一种从眼底图像中有效提取血管的自动化方法。