Skip to main content

Advertisement

SpringerLink
Log in

Feasibility of differential geometry-based features in detection of anatomical feature points on patient surfaces in range image-guided radiation therapy

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

To investigate the feasibility of differential geometry features in the detection of anatomical feature points on a patient surface in infrared-ray-based range images in image-guided radiation therapy.

Methods

The key technology was to reconstruct the patient surface in the range image, i.e., point distribution with three-dimensional coordinates, and characterize the geometrical shape at every point based on curvature features. The region of interest on the range image was extracted by using a template matching technique, and the range image was processed for reducing temporal and spatial noise. Next, a mathematical smooth surface of the patient was reconstructed from the range image by using a non-uniform rational B-splines model. The feature points were detected based on curvature features computed on the reconstructed surface. The framework was tested on range images acquired by a time-of-flight (TOF) camera and a Kinect sensor for two surface (texture) types of head phantoms A and B that had different anatomical geometries. The detection accuracy was evaluated by measuring the residual error, i.e., the mean of minimum Euclidean distances (MMED) between reference (ground truth) and detected feature points on convex and concave regions.

Results

The MMEDs obtained using convex feature points for range images of the translated and rotated phantom A were \(1.79 \pm 0.53\) and \(1.97\pm 0.21\,\hbox {mm}\), respectively, using the TOF camera. For the phantom B, the MMEDs of the convex and concave feature points were \(0.26\pm 0.09\) and \(0.52\pm 0.12\) mm, respectively, using the Kinect sensor. There was a statistically significant difference in the decreased MMED for convex feature points compared with concave feature points \((P<0.001)\).

Conclusions

The proposed framework has demonstrated the feasibility of differential geometry features for the detection of anatomical feature points on a patient surface in range image-guided radiation therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Goyal S, Kataria T (2014) Image guidance in radiation therapy: techniques and applications. Radiol Res Pract. doi:10.1155/2014/705604

  2. Shirato H, Shimizu S, Shimizu T, Nishioka T, Miyasaka K (1999) Real-time tumor-tracking radiotherapy. Lancet 353(9161):1331–1332. doi:10.1016/S0140-6736(99)00700-X

    Article  CAS  PubMed  Google Scholar 

  3. Alasti H, Petric MP, Catton CN, Warde PR (2001) Portal imaging for evaluation of daily on-line setup errors and off-line organ motion during conformal irradiation of carcinoma of the prostate. Int J Radiat Oncol Biol Phys 49(3):869–884. doi:10.1016/S0360-3016(00)01446-2

    Article  CAS  PubMed  Google Scholar 

  4. Wagner TH, Meeks SL, Bova FJ, Friedman WA, Willoughby TR, Kupelian PA, Tome W (2007) Optical tracking technology in stereotactic radiation therapy. Med Dosim 32:111–120. doi:10.1016/j.meddos.2007.01.008

    Article  PubMed  Google Scholar 

  5. Meeks SL, Tomé WA, Willoughby TR, Kupelian PA, Wagner TH, Buatti JM, Bova FJ (2005) Optically guided patient positioning techniques. Semin Radiat Oncol 15:192–201. doi:10.1016/j.semradonc.2005.01.004

    Article  PubMed  Google Scholar 

  6. Yoshitake T, Nakamura K, Shioyama Y, Nomoto S, Ohga S, Toba T, Shiinoki T, Anai S, Terashima H, Kishimoto J, Honda H (2008) Breath-hold monitoring and visual feedback for radiotherapy using a charge-coupled device camera and a head-mounted display: system development and feasibility. Radiat Med 26:50–55. doi:10.1007/s11604-007-0189-4

    Article  PubMed  Google Scholar 

  7. Wang LT, Solberg TD, Medin PM, Boone R (2001) Infrared patient positioning for stereotactic radiosurgery of extracranial tumors. Comput Biol Med 31(2):101–111. doi:10.1016/S0010-4825(00)00026-3

    Article  CAS  PubMed  Google Scholar 

  8. Kang HJ, Grelewicz Z, Wiersma RD (2012) Development of an automated region of interest selection method for 3D surface monitoring of head motion. Med Phys 39(6):3270–3282. doi:10.1118/1.4711805

    Article  CAS  PubMed  Google Scholar 

  9. Schaerer J, Fassi A, Riboldi M, Cerveri P, Baroni G, Sarrut D (2012) Multi-dimensional respiratory motion tracking from markerless optical surface imaging based on deformable mesh registration. Phys Med Biol 57:357–373. doi:10.1088/0031-9155/57/2/357

    Article  PubMed  Google Scholar 

  10. Li G, Ballangrud A, Chan M, Ma R, Beal K, Yamada Y, Chan T, Lee J, Parhar P, Mechalakos J, Hunt M (2015) Clinical experience with two frameless stereotactic radiosurgery (fSRS) systems using optical surface imaging for motion monitoring. J Appl Clin Med Phys 16(4):149–162. doi:10.1120/jacmp.v16i4.5416

    Google Scholar 

  11. Li G, Ballangrud Å, Kuo LC, Kang H, Kirov A, Lovelock M, Yamada Y, Mechalakos J, Amols H (2011) Motion monitoring for cranial frameless stereotactic radiosurgery using video-based three-dimensional optical surface imaging. Med Phys 38(7):3981–3994. doi:10.1118/1.3596526

    Article  PubMed  Google Scholar 

  12. Brahme A, Nyman P, Skatt B (2008) 4D laser camera for accurate patient positioning, collision avoidance, image fusion and adaptive approaches during diagnostic and therapeutic procedures. Med Phys 35(5):1670–1681. doi:10.1118/1.2889720

    Article  PubMed  Google Scholar 

  13. Hui-Hsin H, Hui-Yun C, Chih-I H, Wan-Yuo G, Yu-Te Wu (2013) Shape and curvedness analysis of brain morphology using human fetal magnetic resonance images in utero. Brain Struct Funct 218:1451–1462. doi:10.1007/s00429-012-0469-3

    Article  Google Scholar 

  14. Wang Y, Peterson BS, Staib LH (2000) Shape-based 3D surface correspondence using geodesics and local geometry. In: Proceedings of the CVPR, pp 644–651. doi:10.1109/CVPR.2000.854933

  15. Cerveri P, Manzotti A, Vanzulli A, Baroni G (2014) Local shape similarity and mean-shift curvature for deformable surface mapping of anatomical structures. IEEE Trans Biomed Eng 61(1):16–24. doi:10.1109/TBME.2013.2274672

    Article  PubMed  Google Scholar 

  16. dos Santos TR, Seitel A, Kilgus T, Suwelack S, Wekerle AL, Kengott H, Speidel S, Schlemmer HP, Meinzer HP, Maier-Hein L (2014) Pose-independent surface matching for intra-operative soft-tissue marker-less registration. Med Image Anal 18(7):1101–1114. doi:10.1016/j.media.2014.06.002

    Article  PubMed  Google Scholar 

  17. Segundo M, Silva L, Bellon OP, Quierolo C (2010) Automatic face segmentation and facial landmark detection in range images. IEEE Trans Syst Man Cybern B Cybern 40(5):1319–1330. doi:10.1109/TSMCB.2009.2038233

    Article  Google Scholar 

  18. Passalis G, Perakis P, Theoharis T, Kakadiaris I (2011) Using facial symmetry to handle pose variations in real-world 3D face recognition. IEEE Trans Pattern Anal Mach Intell 33(10):1938–1951. doi:10.1109/TPAMI.2011.49

    Article  PubMed  Google Scholar 

  19. Sukno FM, Waddington JL, Whelan PF (2012) 3D facial landmark localization using combinatorial search and shape regression. In: Proceedings of the ECCV, pp 32–41. doi:10.1007/978-3-642-33863-2_4

  20. Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82(1):35–45. doi:10.1115/1.3662552

  21. Placht S, Stancanello J, Schaller C, Balda M, Angelopoulou E (2012) Fast time-of-flight camera based surface registration for radiotherapy patient positioning. Med Phys 39(1):4–17. doi:10.1118/1.3664006

    Article  PubMed  Google Scholar 

  22. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of the ICCV, pp 839-846. doi:10.1109/ICCV.1998.710815

  23. Paris S, Durand F (2009) A fast approximation of the bilateral filter using a signal processing approach. Int J Comput Vis 81(1):24–52. doi:10.1007/s11263-007-0110-8

    Article  Google Scholar 

  24. Chen C (2007) Bilateral filter (Version 1.11). http://people.csail.mit.edu/jiawen/. Accessed 3 Nov 2015

  25. Agam G, Tang X (2005) A sampling framework for accurate curvature estimation in discrete surfaces. IEEE Trans Vis Comput Graph 11(5):573–583. doi:10.1109/TVCG.2005.69

    Article  PubMed  Google Scholar 

  26. Piegl L, Tiller W (1997) The NURBS book, 2nd edn. Springer, Berlin

    Book  Google Scholar 

  27. Pressley A (2010) Elementary differential geometry, 2nd edn. Springer, London

    Book  Google Scholar 

  28. Koenderink JJ, van Doorn AJ (1992) Surface shape and curvature scales. Img Vis Comput 10(8):557–565. doi:10.1016/0262-8856(92)90076-F

    Article  Google Scholar 

  29. Peng JL, Liu C, Chen Y, Amdur RJ, Vanek K, Li JG (2011) Dosimetric consequences of rotational setup errors with direction simulation in a treatment planning system for fractionated stereotactic radiotherapy. J Appl Clin Med Phys 12(3):61–70

    Google Scholar 

  30. Zhuang Z, Landsittel D, Benson S, Roberge R, Shaffer R (2010) Facial anthropometric differences among gender, ethnicity and age groups. Ann Occup Hyg 54(4):391–402. doi:10.1093/annhyg/meq007

    Article  PubMed  Google Scholar 

  31. Kim S, Akpati HC, Kielbasa JE, Li JG, Liu C, Amdur RJ, Palta JR (2004) Evaluation of intra-fraction patient movement for CNS and head & neck IMRT. Med Phys 31(3):500–506

    Article  PubMed  Google Scholar 

  32. Hussman S, Hermanski A, Edeler T (2011) Real-time motion artifact suppression in TOF camera systems. IEEE Trans Instrum Meas 60(5):1682–1690. doi:10.1109/TIM.2010.2102390

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (B), KAKENHI (No. 23390302), November 2011–March 2014. The authors are grateful to all members in Prof. Arimura’s laboratory (http://web.shs.kyushu-u.ac.jp/~arimura) for their comments and suggestions.

Author information

Authors and Affiliations

  1. Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Mazen Soufi

  2. Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Hidetaka Arimura & Fukai Toyofuku

  3. Hamamatsu University School of Medicine, 1-20-1, Handayama, Higashi-ku, Shizuoka, 431-3192, Japan

    Katsumasa Nakamura

  4. Bandung Institute of Technology, Bandung, 40116, Indonesia

    Fauzia P. Lestari & Freddy Haryanto

  5. Kyushu University Hospital, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Taka-aki Hirose & Yoshiyuki Umedu

  6. Saga Heavy Ion Medical Accelerator, 415, Harakoga-machi, Tosu, 841-0071, Japan

    Yoshiyuki Shioyama

Authors
  1. Mazen Soufi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hidetaka Arimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katsumasa Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fauzia P. Lestari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Freddy Haryanto
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Taka-aki Hirose
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yoshiyuki Umedu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yoshiyuki Shioyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fukai Toyofuku
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hidetaka Arimura.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Statement on human rights and the welfare of animals

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soufi, M., Arimura, H., Nakamura, K. et al. Feasibility of differential geometry-based features in detection of anatomical feature points on patient surfaces in range image-guided radiation therapy. Int J CARS 11, 1993–2006 (2016). https://doi.org/10.1007/s11548-016-1436-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1436-x

Keywords

Search

Navigation