Bioinformatics Institute, A*STAR, Singapore, Singapore.
BMC Bioinformatics. 2014 Jan 18;15:20. doi: 10.1186/1471-2105-15-20.
Structural study of retinal blood vessels provides an early indication of diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks.
In this paper, we consider a novel graph-based approach to address this tracing with crossover problem: After initial steps of segmentation and skeleton extraction, its graph representation can be established, where each segment in the skeleton map becomes a node, and a direct contact between two adjacent segments is translated to an undirected edge of the two corresponding nodes. The segments in the skeleton map touching the optical disk area are considered as root nodes. This determines the number of trees to-be-found in the vessel network, which is always equal to the number of root nodes. Based on this undirected graph representation, the tracing problem is further connected to the well-studied transductive inference in machine learning, where the goal becomes that of properly propagating the tree labels from those known root nodes to the rest of the graph, such that the graph is partitioned into disjoint sub-graphs, or equivalently, each of the trees is traced and separated from the rest of the vessel network. This connection enables us to address the tracing problem by exploiting established development in transductive inference. Empirical experiments on public available fundus image datasets demonstrate the applicability of our approach.
We provide a novel and systematic approach to trace retinal vessel trees with the present of crossovers by solving a transductive learning problem on induced undirected graphs.
视网膜血管的结构研究为糖尿病性视网膜病变、青光眼和高血压性视网膜病变等疾病提供了早期迹象。这些研究需要从眼底图像中自动准确地追踪视网膜血管树结构。然而，现有的工作在处理血管网络中常见的交叉问题时遇到了很大的困难。
在本文中，我们考虑了一种基于图的新方法来解决这个具有交叉问题的追踪问题：在分割和骨架提取的初始步骤之后，可以建立其图形表示，其中骨架图中的每个片段都成为一个节点，并且两个相邻片段之间的直接接触被转换为两个对应节点的无向边。在骨架图中触及视盘区域的片段被视为根节点。这确定了要在血管网络中找到的树的数量，该数量始终等于根节点的数量。基于此无向图表示，追踪问题进一步与机器学习中研究良好的转导推理相关联，目标是从已知的根节点适当地传播树标签到图的其余部分，使得图被分成不相交的子图，或者等效地，追踪和分离每棵树从血管网络的其余部分。这种联系使我们能够通过利用转导推理中的现有发展来解决追踪问题。在公共眼底图像数据集上的实验证明了我们的方法的适用性。
我们通过在诱导的无向图上解决转导学习问题，为存在交叉的视网膜血管树提供了一种新颖而系统的追踪方法。
