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Open Access Highly Accessed Research article

Tracing retinal vessel trees by transductive inference

Jaydeep De12, Huiqi Li3 and Li Cheng14*

Author Affiliations

1 Bioinformatics Institute, A*STAR, Singapore, Singapore

2 School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

3 , Beijing Institute of Technology, Beijing, China

4 School of Computing, National University of Singapore, Singapore, Singapore

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BMC Bioinformatics 2014, 15:20  doi:10.1186/1471-2105-15-20

Published: 18 January 2014

Abstract

Background

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.

Results

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.

Conclusions

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.