Advertisement

A projected landmark method for reduction of registration error in image-guided surgery systems

  • Nasim Dadashi Serej
  • Alireza Ahmadian
  • Saeed Mohagheghi
  • Seyed Musa Sadrehosseini
Original Article

Abstract

Purpose

Image-guided surgery systems are limited by registration error, so practical and effective methods to improve accuracy are necessary. A projection point-based method for reducing the surface registration error in image-guided surgery was developed and tested.

Methods

Checkerboard patterns are projected on visible surfaces to create projected landmarks over a region of interest. Surface information thus becomes available in the form of point clouds of surface point coordinates with submillimeter resolution. The reconstructed 3D point cloud is registered using iterative closest point (ICP) approximation to a 3D point cloud extracted from preoperative CT images of the same region of interest. The projected landmark surface registration method was compared with two other methods using a facial surface phantom: (a) landmark registration using anatomical features, and (b) surface matching based on an additional 40 surface points.

Results

The mean error for the projected landmark surface registration method was 0.64 mm, which was 47.4 and 35.3 % lower relative to mean errors of the anatomical landmark registration and the surface-matching methods, respectively. After applying the proposed method, using target registration error as a gold standard, the resulting mean error was 1.1 mm or a reduction of 61.2 % compared to the anatomical landmark registration.

Conclusion

Optical checkerboard pattern projection onto visible surfaces was used to acquire surface point clouds for image-guided surgery registration. A projected landmark method eliminated the effects of unwanted and overlapping points by acquiring the desired points at specific locations. The results were more accurate than conventional landmark or surface registration.

Keywords

Image-guided surgery Target registration error Surface registration Projected landmarks 

Notes

Acknowledgments

This Project was supported by the Research Center for Biomedical Technology and Robotics (RCBTR), Tehran, Iran and was filed as a provisional patent by the US Patent Office under Parsiss Co. license on August 20, 2012 (Application No.: 61691129; Confirmation No.: 7226).

References

  1. 1.
    Fitzpatrick JM, Hill DLG, Maurer CR (2000) Handbook of medical imaging: medical image processing and analysis. In: Beutel J, Sonka M (eds) Handbook of medical imaging, vol 2. SPIE Press, BellinghamGoogle Scholar
  2. 2.
    Maintz JB, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2:1–36CrossRefPubMedGoogle Scholar
  3. 3.
    Nakajima S, Atsumi H, Kikinis R, Moriarty TM, Metcalf DC, Jolesz FA et al (1997) Use of cortical surface vessel registration for image-guided neurosurgery. Neurosurgery 40:1201–1210CrossRefPubMedGoogle Scholar
  4. 4.
    Fitzpatrick JM, West JB, Maurer CR Jr (1998) Predicting error in rigid-body point-based registration. IEEE Trans Med Imaging 17:694–702CrossRefPubMedGoogle Scholar
  5. 5.
    Sotiras A, Davatzikos C, Paragios N (2013) Deformable medical image registration: a survey. IEEE Trans Med Imaging 32:1153–1190CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    West JB, Fitzpatrick JM, Toms SA, Maurer CR Jr, Maciunas RJ (2001) Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery 48:810–816 discussion 816–7PubMedGoogle Scholar
  7. 7.
    Labadie RF, Davis BM, Fitzpatrick JM (2005) Image-guided surgery: What is the accuracy? Curr Opin Otolaryngol Head Neck Surg 13:27–31CrossRefPubMedGoogle Scholar
  8. 8.
    Labadie RF, Shah RJ, Harris SS, Cetinkaya E, Haynes DS, Fenlon MR et al (2005) In vitro assessment of image-guided otologic surgery: submillimeter accuracy within the region of the temporal bone. Otolaryngol Head Neck Surg 132:435–442CrossRefPubMedGoogle Scholar
  9. 9.
    Hill DL, jr Maurer CR, Studholme C, Fitzpatrick JM, Hawkes DJ (1998) Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm. J Comput Assist Tomogr 22:317–323CrossRefPubMedGoogle Scholar
  10. 10.
    Ershad M, Ahmadian A, Dadashi Serej N, Saberi H, Amini Khoiy K (2014) Minimization of target registration error for vertebra in image-guided spine surgery. Int J Comput Assist Radiol Surg 9:29–38CrossRefPubMedGoogle Scholar
  11. 11.
    Widmann G, Stoffner R, Bale R (2009) Errors and error management in image-guided craniomaxillofacial surgery. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 107:701–715CrossRefPubMedGoogle Scholar
  12. 12.
    Widmann G, Stoffner R, Sieb M, Bale R (2009) Target registration and target positioning errors in computer-assisted neurosurgery: proposal for a standardized reporting of error assessment. Int J Med Robot 5:355–365CrossRefPubMedGoogle Scholar
  13. 13.
    Bale RJ, Burtscher J, Eisner W, Obwegeser AA, Rieger M, Sweeney RA et al (2000) Computer-assisted neurosurgery by using a noninvasive vacuum-affixed dental cast that acts as a reference base: another step toward a unified approach in the treatment of brain tumors. J Neurosurg 93:208–213CrossRefPubMedGoogle Scholar
  14. 14.
    Labadie RF, Shah RJ, Harris SS, Cetinkaya E, Haynes DS, Fenlon MR et al (2004) Submillimetric target-registration error using a novel, non-invasive fiducial system for image-guided otologic surgery. Comput Aided Surg 9:145–153CrossRefPubMedGoogle Scholar
  15. 15.
    Lee JD, Huang CH, Wang ST, Lin CW, Lee ST (2010) Fast-MICP for frameless image-guided surgery. Med Phys 37:4551–4559CrossRefPubMedGoogle Scholar
  16. 16.
    Nazem F, Ahmadian A, Seraj ND, Giti M (2014) Two-stage point-based registration method between ultrasound and CT imaging of the liver based on ICP and unscented Kalman filter: a phantom study. Int J Comput Assist Radiol Surg 9:39–48CrossRefPubMedGoogle Scholar
  17. 17.
    Moghari MH, Abolmaesumi P (2006) Comparing unscented and extended Kalman filter algorithms in the rigid-body point-based registration. Conf Proc IEEE Eng Med Biol Soc 1:497–500CrossRefPubMedGoogle Scholar
  18. 18.
    Audette MA, Ferrie FP, Peters TM (2000) An algorithmic overview of surface registration techniques for medical imaging. Med Image Anal 4:201–217CrossRefPubMedGoogle Scholar
  19. 19.
    Myronenko A, Song X (2010) Point set registration: coherent point drift. IEEE Trans Pattern Anal Mach Intell 32:2262–2275CrossRefPubMedGoogle Scholar
  20. 20.
    Grimson WL, Ettinger GJ, White SJ, Lozano-Perez T, Wells WM, Kikinis R (1996) An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization. IEEE Trans Med Imaging 15:129–140CrossRefPubMedGoogle Scholar
  21. 21.
    Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256CrossRefGoogle Scholar
  22. 22.
    Parra NA (2009) Rigid and non-rigid point-based medical image registration, FIU Electronic Theses and DissertationsGoogle Scholar
  23. 23.
    Kjer HM, Wilm J (2010) Evaluation of surface registration algorithms for PET motion correction. Bachelor, Kongens LyngbyGoogle Scholar
  24. 24.
    Farnia P, Ahmadian A, Khoshnevisan A, Jaberzadeh A, Serej ND, Kazerooni AF (2011) An efficient point based registration of intra-operative ultrasound images with MR images for computation of brain shift; a phantom study. Conf Proc IEEE Eng Med Biol Soc 2011:8074–8077PubMedGoogle Scholar
  25. 25.
    Ahmadian A, Dadashi Serej N, Karimifard S, Farnia P (2013) An efficient method for estimation of soft tissue deformation based on intra-operative stereo image features and point-based registration. Int J Imaging Syst Technol 23:294–303CrossRefGoogle Scholar
  26. 26.
    West JB, Fitzpatrick JM, Toms SA, Maurer CR Jr, Maciunas RJ (2001) Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery 48:810–817PubMedGoogle Scholar
  27. 27.
    Micron Tracker Manual, in Claron Inc., edGoogle Scholar
  28. 28.
    Breunig MM, Kriegel H-P, Ng RT, Sander R (2000) LOF: identifying density-based local outliers. SIGMOD Rec 29:93–104 Google Scholar
  29. 29.
    Marmulla R, Eggers G, Mühling J (2005) Laser surface registration for lateral skull base surgery. Minim Invasive Neurosurg 48:181–185CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  • Nasim Dadashi Serej
    • 1
  • Alireza Ahmadian
    • 1
  • Saeed Mohagheghi
    • 2
  • Seyed Musa Sadrehosseini
    • 3
  1. 1.Department of Medical Physics and Biomedical Engineering, Image Guided Intervention Group, Research Centre for Biomedical Technology & RoboticsRCBTR, Tehran University of Medical SciencesTehranIran
  2. 2.Research and Development Section (R&D)Parseh Intelligent Surgical System Company, Parsiss Co.TehranIran
  3. 3.Skull Base Center, Imam Khomeini Hospital ComplexTehran University of Medical SciencesTehranIran

Personalised recommendations