A vascular image registration method based on network structure and circuit simulation

Abstract

BackgroundImage registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method.ResultsDifferent from common image registration methods based on area or features, which were sensitive to distortion and uncertainty in vascular structure, we proposed a novel registration method based on network structure and circuit simulation. Vessel images were transformed to graph networks and segmented to branches to reduce the calculation complexity. Weighted graph networks were then converted to circuits, in which node voltages of the circuit reflecting the vessel structures were used for node registration. The experiments in the two-dimensional and three-dimensional simulation and clinical image sets showed the success of our proposed method in registration.ConclusionsThe proposed vascular image registration method based on network structure and circuit simulation is stable, fault tolerant and efficient, which is a useful complement to the current mainstream image registration methods.

A Review on Registration of Medical Images Using Graph Theoretic Approaches

“…First the salient points are identified and a circuit conversion model is proposed where the spatial difference is reflected as the node voltage. Network decomposition is done using the NSI method and the branching criteria is used to deal with large scale networks [28]. Parekar…”

Optimal transport applied to eye fundus image registration

“…Thus, the retinal image registration problem is converted into an edge-to-edge correspondence problem. Similar to Deng et al (2010), Chen et al (2017) presented a novel vascular image registration method based on network structure index and circuit simulation. Besides, Serradell et al (2015) introduced a method for matching two-and three-dimensional graph structures extracted from microscopy image stacks, angiography data and retinal fundus images, which the authors called Active Testing Search for Robust Graph Matching (ATS-RGM).…”