TY - GEN
T1 - Tracing retinal blood vessels by matrix-forest theorem of directed graphs
AU - Cheng, Li
AU - De, Jaydeep
AU - Zhang, Xiaowei
AU - Lin, Feng
AU - Li, Huiqi
PY - 2014
Y1 - 2014
N2 - This paper aims to trace retinal blood vessel trees in fundus images. This task is far from being trivial as the crossover of vessels are commonly encountered in image-based vessel networks. Meanwhile it is often crucial to separate the vessel tree structures in applications such as diabetic retinopathy analysis. In this work, a novel directed graph based approach is proposed to cast the task as label propagation over directed graphs, such that the graph is to be partitioned into disjoint sub-graphs, or equivalently, each of the vessel trees is traced and separated from the rest of the vessel network. Then the tracing problem is addressed by making novel usage of the matrix-forest theorem in algebraic graph theory. Empirical experiments on synthetic as well as publicly available fundus image datasets demonstrate the applicability of our approach.
AB - This paper aims to trace retinal blood vessel trees in fundus images. This task is far from being trivial as the crossover of vessels are commonly encountered in image-based vessel networks. Meanwhile it is often crucial to separate the vessel tree structures in applications such as diabetic retinopathy analysis. In this work, a novel directed graph based approach is proposed to cast the task as label propagation over directed graphs, such that the graph is to be partitioned into disjoint sub-graphs, or equivalently, each of the vessel trees is traced and separated from the rest of the vessel network. Then the tracing problem is addressed by making novel usage of the matrix-forest theorem in algebraic graph theory. Empirical experiments on synthetic as well as publicly available fundus image datasets demonstrate the applicability of our approach.
UR - http://www.scopus.com/inward/record.url?scp=84906988590&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10404-1_78
DO - 10.1007/978-3-319-10404-1_78
M3 - Conference contribution
C2 - 25333171
AN - SCOPUS:84906988590
SN - 9783319104034
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 626
EP - 633
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -
