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Vesselness filters: A survey with benchmarks applied to liver imaging

Vesselness filters: A survey with benchmarks applied to liver imaging

Abstract:  The accurate knowledge of vascular network geometry is crucial for many clinical applications such as cardiovascular disease diagnosis and surgery planning. Vessel enhancement algorithms are often a key step to improve the robustness of vessel segmentation. A wide variety of enhancement filters exists in the literature, but they are often difficult to compare as the applications and datasets differ from a paper to another and the code is rarely available. In this article, we compare seven vessel enhancement filters covering the last twenty years literature in a unique common framework. We focus our study on the liver vascular network which is under-represented in the literature. The evaluation is made from three points of view: in the whole liver, in the vessel neighborhood and near the bifurcations. The study is performed on two publicly available datasets: the Ircad dataset (CT images) and the VascuSynth dataset adapted for MRI simulation. We discuss the strengths and weaknesses of each method in the hepatic context. In addition, the benchmark framework including a C++ implementation of each compared method is provided. An online demonstration ensures the reproducibility of the results without requiring any additional software.

Keywords: Vesselness, liver, benchmark

Reference: Jonas Lamy, Odyssée Merveille, Bertrand Kerautret, Nicolas Passat, Antoine Vacavant. Vesselness filters: A survey with benchmarks applied to liver imaging, ICPR 2020.

Download: https://hal.archives-ouvertes.fr/hal-02544493v2

Component-graph construction

Component-graph construction

Abstract: Component-trees are classical tree structures for grey-level image modelling. Component graphs are defined as a generalization of component trees to images taking their values in any (totally or partially) ordered sets. Similarly to component-trees, component-graphs are a lossless image model; then, they can allow for the development of various image processing approaches. However, component-graphs are not trees, but directed acyclic graphs. This makes their construction non-trivial, leading to non-linear time cost and resulting in non-linear space data structures. In this theoretical article, we discuss the notion(s) of component-graph, and we propose a strategy for their efficient building and representation, which are necessary conditions for further involving them in image processing approaches.

Keywords: Component-graph, algorithmics , mathematical morphology, multivalued images

Reference: Nicolas Passat, Benoît Naegel, Camille Kurtz. Component-graph construction. Journal of Mathematical Imaging and Vision, 2019.

Download: https://hal.archives-ouvertes.fr/hal-01821264v1