Combination of Image Registration Algorithms for Patient Alignement in Proton Beam Therapy

  • Rachid Belaroussi
  • Guillaume Morel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

We propose a measure of patient alignment in a video by combining different image representations : grey level, edges, and a set of feature points. When patient head is correctly positionned, a reference image with its ellipse is stored as a template of correct alignment. Edges detection results in a second template of the correct head location. Corners inside the ellipse are detected and tracked: a set of N feature points composes a third template. Template matching computes a measure of similarity between a representation of the reference image and a window sliding around the reference location. Similarity with these three models are combined by the product rule. Location of window the most similar to the templates gives the translation T of the reference model in the image plane. This measure of patient misalignment could avoid X-ray verification of patient alignment, reducing patient dose and duration of treatment sessions.

Keywords

Proton beam therapy expert combination template matching feature points color model camshift 
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References

  1. 1.
    Bradsky, G.: Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal (1998)Google Scholar
  2. 2.
    Bouguet, J.-Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker: Description of the algorithm. Technical report, Intel Corporation Microprocessor Research Labs (2000)Google Scholar
  3. 3.
    Shi, J., Tomasi, C.: Good Features to Track. In: Conference on Computer Vision and Pattern Recognition (1994)Google Scholar
  4. 4.
    Milgram, M., Belaroussi, R., Prevost, L.: Multi-stage Combination of Geometric and Colorimetric Detectors for Eyes Localization. In: 13th International Conference Image Analysis and Processing, pp. 1010–1017 (2005)Google Scholar
  5. 5.
    Kittler, J., Hatef, M., Duin, R., Matas, J.: On Combining Classifiers. Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  6. 6.
    Pinault, S., Morel, G., Ferrand, R., Auger, M., Mabit, C.: Using an external registration system for daily patient repositioning in protontherapy. In: International Conference on Intelligent Robots and Systems (2007)Google Scholar
  7. 7.
    Fuller, D.C., Thomas, C.R., Schwartz, S., Golden, N., Ting, J., Wong, A., Erdogmus, D., Scarbrough, T.J.: Method comparison of ultrasound and kilovoltage x-ray fiducial marker imaging for prostate radiotherapy targeting. Phys. Med. Biol. 51(19), 4981–4993 (2006)CrossRefGoogle Scholar
  8. 8.
    Baronia, G., Ferrignoa, G., Orecchia, R., Pedotti, A.: Real-time three-dimensional motion analysis for patient positioning verification. Radiotherapy and Oncology 54 (2000)Google Scholar
  9. 9.
    de Kock, E.A., Muller, N., Maartens, D., van der Merwe, J., Muller, D., van Rooyen, R., van der Merwe, A., Eksteen, J., von Hoesslin, N., Wagener, D., Hough, J.: Integrating an industrial robot and multi-camera computer vision systems into a patient positioning system for high-precision radiotherapy. In: ISR Symposium (2004)Google Scholar
  10. 10.
    Harms, W., Schoffel, P.J., Sroka-Perez, G., Schlegel, W., Karger, C.P.: Accuracy of a commercial optical 3D surface imaging system for rea- lignment of patients for radiotherapy of the thorax. Phys. Med. Biol. 52(5), 3949–3963 (2007)Google Scholar
  11. 11.
    Tanga, J., Dieterichb, S., Clearya, K.: Respiratory Motion Tracking of Skin and Liver in Swine for CyberKnife Motion Compensation. In: SPIE Medical Imaging (2004)Google Scholar
  12. 12.
    Verellen, D., Soete, G., Linthout, N., Van Acker, S., De Roover, P., Van de Steene, J., Vinh-Hung, V., Storme, G.: Quality assurance of a system for improved target localization and patient setup that combines real time infrared tracking and stereoscopic Xray imaging. Radiotherapy and Oncology 67(5), 129–141 (2003)CrossRefGoogle Scholar
  13. 13.
    Gerszten, P.C., Ozhasoglu, C., Burton, S.A., Welch, W.C., Vogel, W.J., Atkins, B.A., Kalnicki, S.: CyberKnife frameless single fraction ste- reotactic radiosurgery for tumors of the sacrum. Neurosurg. 15(2) (2003)Google Scholar
  14. 14.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  15. 15.
    Valet, L., Mauris, G., Bolon, P.: A Statistical Overview of recent literature in Information Fusion. IEEE Magazine on Aeronautics and Electronics Systems 16(3), 7–14 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rachid Belaroussi
    • 1
  • Guillaume Morel
    • 1
  1. 1.Institut des Systèmes Intelligents et Robotique ParisFrance

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