Image-Guided Procedures, Robotic Interventions, and Modeling

Superficial vessel reconstruction with a multiview camera system

[+] Author Affiliations
Filipe M. M. Marreiros

Linköping University, Center for Medical Image Science and Visualization, Campus US, Linköping SE-581 85, Sweden

Linköping University, Department of Science and Technology—Media and Information Technology, Campus Norrköping, Norrköping SE-601 74, Sweden

Linköping University, Department of Medical and Health Sciences, Campus US, Linköping SE-581 85, Sweden

Sandro Rossitti, Per M. Karlsson

County Council of Östergötland, Department of Neurosurgery, Linköping University, Campus US, Linköping SE-581 85, Sweden

Chunliang Wang

Linköping University, Center for Medical Image Science and Visualization, Campus US, Linköping SE-581 85, Sweden

Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge SE-141 52, Sweden

Torbjörn Gustafsson, Per Carleberg

XM Reality AB, Diskettgatan 11B, Linköping SE-583 35, Sweden

Örjan Smedby

Linköping University, Center for Medical Image Science and Visualization, Campus US, Linköping SE-581 85, Sweden

Linköping University, Department of Science and Technology—Media and Information Technology, Campus Norrköping, Norrköping SE-601 74, Sweden

Linköping University, Department of Medical and Health Sciences, Campus US, Linköping SE-581 85, Sweden

Royal Institute of Technology, School of Technology and Health, Alfred Nobels Allé 10, Huddinge SE-141 52, Sweden

J. Med. Imag. 3(1), 015001 (Jan 05, 2016). doi:10.1117/1.JMI.3.1.015001
History: Received July 22, 2015; Accepted November 23, 2015
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Abstract.  We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are 1  mm.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Filipe M. M. Marreiros ; Sandro Rossitti ; Per M. Karlsson ; Chunliang Wang ; Torbjörn Gustafsson, et al.
"Superficial vessel reconstruction with a multiview camera system", J. Med. Imag. 3(1), 015001 (Jan 05, 2016). ; http://dx.doi.org/10.1117/1.JMI.3.1.015001


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Superficial vessel reconstruction with a multiview camera system. J Med Imaging (Bellingham) 2016;3(1):015001.
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