一种从视网膜图像中检测血管的新方法。
A novel method for blood vessel detection from retinal images.
机构信息
School of Biomedical Engineering, Capital Medical University, Beijing, China.
出版信息
Biomed Eng Online. 2010 Feb 28;9:14. doi: 10.1186/1475-925X-9-14.
BACKGROUND
The morphological changes of the retinal blood vessels in retinal images are important indicators for diseases like diabetes, hypertension and glaucoma. Thus the accurate segmentation of blood vessel is of diagnostic value.
METHODS
In this paper, we present a novel method to segment retinal blood vessels to overcome the variations in contrast of large and thin vessels. This method uses adaptive local thresholding to produce a binary image then extract large connected components as large vessels. The residual fragments in the binary image including some thin vessel segments (or pixels), are classified by Support Vector Machine (SVM). The tracking growth is applied to the thin vessel segments to form the whole vascular network.
RESULTS
The proposed algorithm is tested on DRIVE database, and the average sensitivity is over 77% while the average accuracy reaches 93.2%.
CONCLUSIONS
In this paper, we distinguish large vessels by adaptive local thresholding for their good contrast. Then identify some thin vessel segments with bad contrast by SVM, which can be lengthened by tracking. This proposed method can avoid heavy computation and manual intervention.
背景
视网膜图像中视网膜血管的形态变化是糖尿病、高血压和青光眼等疾病的重要指标。因此，血管的准确分割具有诊断价值。
方法
本文提出了一种新的方法来分割视网膜血管，以克服大而细的血管对比度的变化。该方法使用自适应局部阈值化生成二值图像，然后提取大的连通分量作为大血管。二值图像中的残余碎片包括一些细的血管段（或像素），通过支持向量机（SVM）进行分类。跟踪生长应用于细的血管段以形成整个血管网络。
结果
该算法在 DRIVE 数据库上进行了测试，平均灵敏度超过 77%，平均准确率达到 93.2%。
结论
本文通过自适应局部阈值化来区分对比度好的大血管，然后通过 SVM 识别一些对比度差的细血管段，这些段可以通过跟踪来延长。该方法可以避免大量的计算和人工干预。
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