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Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint

Chen, Cheng ; Kojcev, Risto ; Haschtmann, Daniel ; Fekete, Tamas ; Nolte, Lutz ; Zheng, Guoyan

In: Journal of Digital Imaging, 2015, vol. 28, no. 4, p. 474-480

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    Title
    • Ruler Based Automatic C-Arm Image Stitching Without Overlapping Constraint
    Author
    • Chen, Cheng. Institute of Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    • Kojcev, Risto. Institute of Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    • Haschtmann, Daniel. Schulthess Clinic, Lengghalde 2, 8008, Zurich, Switzerland
    • Fekete, Tamas. Schulthess Clinic, Lengghalde 2, 8008, Zurich, Switzerland
    • Nolte, Lutz. Institute of Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    • Zheng, Guoyan. Institute of Surgical Technology and Biomechanics, University of Bern, Stauffacherstrasse 78, 3014, Bern, Switzerland
    Document Type
    • Postprint
    Language
    • English
    Published in
    • Journal of Digital Imaging, 2015, vol. 28, no. 4, p. 474-480. Springer US; http://www.springer-ny.com
    Other Version
    OAI-PMH Identifier
    • oai:doc.rero.ch:332789
    Summary
    In this paper, we propose a new method for stitching multiple fluoroscopic images taken by aC-arm instrument. We employ an X-ray radiolucent ruler with numbered graduations while acquiring the images, and the image stitching is based on detecting and matching ruler parts in the images to the corresponding parts of a virtual ruler. To achieve this goal, we first detect the regular spaced graduations on the ruler and the numbers. After graduation labeling, for each image, we have the location and the associated number for every graduation on the ruler. Then, we initialize the panoramic X-ray image with the virtual ruler, and we "paste” each image by aligning the detected ruler part on the original image, to the corresponding part of the virtual ruler on the panoramic image. Our method is based on ruler matching but without the requirement of matching similar feature points in pairwise images, and thus, we do not necessarily require overlap between the images. We tested our method on eight different datasets of X-ray images, including long bones and a complete spine. Qualitative and quantitative experiments show that our method achieves good results.