Title: A fast and robust level set motion-assisted deformable registration method for surgical platform: comparison with conventional methods
Authors: Kim, Daegwan
Kim, Namkug
Kim, Wookeun
Choi, Jongkyun
Song, Teaha
Jeong, Semi
Seo, Joon Beom
Lee, Sangmin
Citation: WSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 1-5.
Issue Date: 2017
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf
ISBN: 978-80-86943-45-9
ISSN: 2464-4617
Keywords: deformovatelná registrace;chirurgická platforma;3D CT obraz;nastavení úrovně pohybu;plicní intervence;vizuální bodování
Keywords in different language: deformable registration;surgical platform;3D CT image;level set motion;lung intervention;visual scoring
Abstract: A level set motion assisted deformable registration method for diagnostic computed tomography (CT) and preoperative CT images were proposed and its accuracy and speed were compared with those of other conventional methods. Fifteen 3D CT images obtained from lung biopsy patients were scanned. Each scan consisted of diagnostic and preoperative CT images. Each deformable registration method was initially evaluated with a landmark-based affine registration algorithm. Various deformable registration methods such as level set motion, demons, diffeomorphic demons, and b-spline were compared. Visual assessment by two expert thoracic radiologists using five scales showed an average visual score of 3.2 for level set motion deformable registration, whereas scores were below 3 for other deformable registration methods. In the qualitative assessment, the level set motion algorithm showed better results than those obtained with other deformable registration methods. A level set motion based deformable registration algorithm was effective for registering diagnostic and preoperative volumetric CT images for image-guided lung intervention.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2017: Short Papers Proceedings

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