In this paper, a new mean matting method based on mean correlation is proposed to extract thin blood vessels precisely. The proposed algorithm is the combination of both supervised and unsupervised method. The supervised methodology performs well in extracting thick blood vessels; however, thin vessels are not precisely extracted. Even the capability of unsupervised method is better in extracting thin vessels; it has some artifacts in the output. The proposed method combines the advantages of both supervised and unsupervised method to extract vessel regions more precisely irrespective of their thickness. Using supervised methodology, thick blood vessels are extracted by training the classifier with the significant features describing the vessel regions. Trimap is generated on the unsupervised output and mean correlation is computed for all unknown pixels in the trimap to classify those pixels into vessels or background. The proposed matting method has less computational complexity compared to other existing matting methods. The performance of the proposed method is evaluated in detail on DRIVE and STARE datasets.

中文翻译：

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使用平均消光法改进视网膜细血管提取

本文提出了一种新的基于平均相关的平均抠图方法来精确提取细血管。所提出的算法是有监督和无监督方法的结合。监督方法在提取粗血管方面表现良好；然而，不能精确提取细血管。即使是无监督方法在提取细血管方面的能力也更好；它在输出中有一些工件。所提出的方法结合了有监督和无监督方法的优点，可以更精确地提取血管区域，而不管其厚度如何。使用监督方法，通过使用描述血管区域的重要特征训练分类器来提取粗血管。Trimap 在无监督输出上生成，并为 Trimap 中的所有未知像素计算平均相关性，以将这些像素分类为血管或背景。与其他现有的抠图方法相比，所提出的抠图方法具有更小的计算复杂度。在 DRIVE 和 STARE 数据集上详细评估了所提出方法的性能。