Warianty tytułu
Spatial measurements in the field of view of a stereoendoscope
Języki publikacji
Abstrakty
W pracy przedstawiono procedurę trójwymiarowej metrycznej rekonstrukcji powierzchni na podstawie zapisu wideo ze stereoendoskopu stosowanego w chirurgii minimalnie inwazyjnej. Metoda bazuje na dopasowaniu obszarami fragmentów obrazów. Wyniki rekonstrukcji porównano z danymi uzyskanymi dla tego samego obiektu metodą referencyjną. Średni błąd rekonstrukcji uzyskany dla poszczególnych klatek sekwencji wynosi od 2,1 do 4,2 mm.
In this work, we present a procedure for 3-dimensional metric surface reconstruction based on video data from a stereoendoscope used in minimally invasive surgery. The reconstruction is based on stereo block matching algorithm. The results of the reconstruction were compared to a reference data set obtained simultaneously for the same object. The mean reconstruction error obtained for individual frames falls within the range of 2.1 to 4.2 mm.
Czasopismo
Rocznik
Tom
Strony
13-16
Opis fizyczny
Bibliogr. 9 poz., rys., wykr.
Twórcy
autor
- AGH Akademia Górniczo-Hutnicza, Katedra Metrologii i Elektroniki, Al. Mickiewicza 30, 30-059 Kraków, adrian.goral@agh.edu.pl
Bibliografia
- [1] Soper T., Porter M., Seibel E., Surface Mosaics of the Bladder Reconstructed from Endoscopic Video for Automated Surveillance, IEEE Transactions on Biomedical Engineering, 59 (2012), n. 6, 1670-1680.
- [2] Morugues F., Devernay F., Coste-Manière E., 3D Reconstruction of the operating field for image overlay in 3-D endoscopic surgery, IEEE and ACM International Symposium on Augmented Reality, (2001), 191-192.
- [3] Wengert C., Bossard L., Häberling A., Baur C., Székely G., Cattin P., Endoscopic Navigation for Minimally Invasive Suturing, Medical Image Computing and Computer Assisted Interventions, 10 (2007), 620-627.
- [4] Stoyanov D., Visentini Scarzanella M., Pratt P., Yang G.-Z., Real-Time Stereo Reconstruction in Robotic Assisted Minimally Invasive Surgery, Medical Image Computing and Computer Assisted Interventions, (2010) 275-282
- [5] Yang B., Wong W., Liu C., Poignet P., 3D soft tissue tracking using spatial-color joint probability distribution and thin-plate spline model, Pattern Recognition, 47 (2004), n. 9, 2962-2973.
- [6] Heikkilä J., Silvén O., A four-step camera calibration procedure with implicit image correction, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (1997) 1106-1112.
- [7] Konolige K., Small Vision Systems: Hardware and Implementation, Robotics Research: The Eighth International Symposium (1998) 203-212.
- [8] Rusu R., Marton Z., Blodow N., Dolha M., Beetz M., Towards 3D Point Cloud based Object Maps for Household Environments, Robotics and Autonomous Systems, 56 (2008), n. 11, 927-941.
- [9] Alexa M., Behr J., Cohen-Or D., Fleishman S., Levin D., Silva C., Computing and Rendering Point Set Surfaces, IEEE Transactions on Visualization and Computer Graphics, 9 (2003), n. 1, 3-15.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-7c105d43-d112-40c0-9d2c-8c51f2e898c2