Skip to main content
SpringerLink
Log in

Tracking Organs Composed of One or Multiple Regions Using Geodesic Active Region Models

  • Chapter
  • First Online:
Recent Advances in the 3D Physiological Human

  • 782 Accesses

Abstract

In radiotherapy treatment it is very important to find out the target organs on the medical image sequence in order to determine and apply the proper dose. The techniques to achieve this goal can be classified into extrinsic and intrinsic. Intrinsic techniques only use image processing with medical images associated to the radiotherapy treatment, as we deal in this chapter. To accurately perform this organ tracking it is necessary to find out segmentation and tracking models that were able to be applied to several image modalities involved on a radiotherapy session (CT , MRI , etc.). The movements of the organs are mainly affected by two factors: breathing and involuntary movements associated with the internal organs or patient positioning. Among the several alternatives to track the organs of interest, a model based on geodesic active regions is proposed. This model has been tested over CT images from the pelvic, cardiac, and thoracic area. A new model for the segmentation of organs composed by more than one region is proposed.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Akgul Y, Kambhamettu C, Stone M (1999) Automatic extraction and tracking of the tongue contours. IEEE Transactions on Medical Imaging 18:1035–1045

    Article  Google Scholar 

  2. Bascle B, Bouthemy P, Deriche R, Meyer F (1994) Suivi de primitives complexes sur une sequence d’Images. Tech. rep., Institut National de Recherche en Informatique et en Automatique (INRIA)

    Google Scholar 

  3. Berbeco R, Jiang S, Sharp G, Chen G, Mostafavi H, Shirato H (2004) Integrated radiotherapy imaging system (IRIS): Design considerations of tumour tracking with linac gantry-mounted diagnostic x-ray systems with flat panel detectors. Physics in Medicine and Biology 49:243–255

    Article  Google Scholar 

  4. Blackall J (2002) Respiratory motion in image-guided interventions of the liver. Ph.D. thesis, Guy’s King’s and St. Thomas’ School of Medicine. King’s College, London

    Google Scholar 

  5. Brewer J, Betke M, Gierga D, Chen G (2004) Real-time 4d tumor tracking and modeling from internal and external fiducials in fluoroscopy. In: Proceedings of 7th International Conference on Medical Image Computing and Computer Assisted Intervention, LNCS, vol. 3217, pp. 594–601

    Google Scholar 

  6. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. International Journal of Computer Vision 1(22):61–79

    Article  Google Scholar 

  7. Castellanos M, Lacornerie T, Prevost B, Mirabel X, Lartigau E (2004) Sistemas de contencion de organos en movimiento: Radioterapia adaptada a la respiracion (RAR). In: Alpa Editores (ed.) 3 Jornadas Oncologicas Internacionales. Alpa Editores S.A., Madrid

    Google Scholar 

  8. Comaniciu D, Zhou XS, Krishnan S (2004) Robust realtime myocardial border tracking for echocardiography: An information fusion approach. IEEE Transactions on Medical Imaging 23(7):849–860

    Article  Google Scholar 

  9. Dieterich S, Tang J, Rodgers J, Cleary K (2003) Skin respiratory motion tracking for stereotactic radiosurgery using the CyberKnife. International Congress Series 1256:130–136

    Article  Google Scholar 

  10. Drumond T, Cipolla R (2002) Real-time tracking of complex structures with on-line camera calibration. Image and Vision Computing 20:427–433

    Article  Google Scholar 

  11. Elgort D, Duerk J (2004) A review of technical advances in interventional magnetic resonance imaging. Academic Radiology 12:1089–1099

    Article  Google Scholar 

  12. Gentile C, Camps O, Sznaier M (2004) Segmentation for robust tracking in the presence of severe occlusion. IEEE Transactions on Image Processing 13(2):166–178

    Article  Google Scholar 

  13. Kang D, Kim C, Seo Y, Kweon I (1999) A fast and stable method for detecting and tracking medical organs in MRI sequences. IEICE Transactions on Informatics and Systems E83-D(2):497–499

    Google Scholar 

  14. Kass M, Witkin A, Terzopopulos D (1987) Snakes: Active contour models. In: First International Conference On Computer Vision, vol. 1, pp. 259–268

    Google Scholar 

  15. Keall P, Mageras G (2004) Managing respiratory motion in radiation oncology. In: AAPM 46th Annual Meeting

    Google Scholar 

  16. Keall P, Joshi S, Vedam S, Siebers J, Kini V, Mohan R (2005) Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. Medical Physics 32(4):945–951

    Article  Google Scholar 

  17. Kocak D, da Vittoria Lobo N, Widder EA (1999) Computer vision techniques for quantifying tracking and identifying bioluminescent plankton. IEEE Journal of Oceanic Engineering 24(1):81–95

    Article  Google Scholar 

  18. Lotjonen J (2001) Segmentation of MR images using deformable models: Applications to cardiac images. International Journal of Bioelectromagnetism 3

    Google Scholar 

  19. Mageras G, Yorke E (2004) Deep inspiration breath hold and respiratory gating strategies for reducing organ motion in radiation treatment. Seminars in Radiation Oncology 14(1):65–75

    Article  Google Scholar 

  20. Malciu M, Preteux F (2000) A robust model-based approach for 3d head tracking in video sequences. In: Proceedings of 4th International Conference on Automatic Face and Gesture Recognition 1, pp. 69–173

    Google Scholar 

  21. Malladi R, Sethian JA, Vemuri BC (1995) Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2):158–175

    Article  Google Scholar 

  22. McInerney T, Terzopoulos D (1995) A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4d image analysis. Journal of Computerized Medical Imaging and Graphics 19:69–83

    Article  Google Scholar 

  23. Minohara S, Kanai T, Endo M, Noda K, Kanazawa M (2000) Respiratory gated irradiation system for heavy-ion radiotherapy. International Journal of Radiation Oncology 47(4):1097–1103

    Article  Google Scholar 

  24. Mukherjee D, Ray N, Acton ST (2004) Level set analysis for leukocyte detection and tracking. IEEE Transactions on Image Processing 13(4):562–572

    Article  Google Scholar 

  25. Murphy M (2004) Tracking moving organs in real time. Seminars in Radiation Oncology 14(1):91–100

    Article  Google Scholar 

  26. Nederveen A (2002) Image guided position verification for intensity modulated radiotherapy of prostate cancer. Ph.D. thesis, University Utrecht

    Google Scholar 

  27. Osher S, Fedkiw R (2001) Level set methods: An overview and some recent results. Journal of Computation Physics 169(2):463–502

    Article  MathSciNet  MATH  Google Scholar 

  28. Paragios N (2002) A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Transactions on Medical Imaging 22(6):773–776

    Article  Google Scholar 

  29. Paragios N, Deriche R (1997) A PDE-based level-set approach for detecting and tracking of moving objects. Rapport de Recherche. INRIA Sophia Antipolis 1(3173):1–29

    Google Scholar 

  30. Paragios N, Deriche R (1999) Geodesic active regions for motion estimation and tracking. Tech. rep., Institut National de Recherche en Informatique et en Automatique

    Google Scholar 

  31. Pardas M, Sayrol E (2001) Motion estimation based tracking of active contours. Pattern Recognition Letters 22:1447–1456

    Article  MATH  Google Scholar 

  32. Rife J, Rock SM (2003) Segmentation methods for visual tracking of deep-ocean jellyfish using a conventional camera. IEEE Journal of Oceanic Engineering 28(4):595–608

    Article  Google Scholar 

  33. Sethian J (1999) Level Set Methods and Fast Marching Methods. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  34. Sharp G, Jiang S, Shimizu S, Shirato H (2004) Prediction of respiratory tumour motion for real-time image-guided radiotherapy. Physics in Medicine and Biology 49:425–440

    Article  Google Scholar 

  35. Shirato H, Seppenwoolde Y, Kitamura K, Onimura R (2004) Intrafractional tumor motion: Lung and liver. Seminars in Radiation Oncology 14(1):10–18

    Article  Google Scholar 

  36. Tang J, Dieterich S, Cleary K (2004) Respiratory motion tracking of skin and liver in swine for CyberKnife motion compensation. In: Proceedings of SPIE Medical Imaging, vol. 5367, pp. 729–734

    Article  Google Scholar 

  37. Tsechpenakis G, Rapantzikos K, Tsapatsoulis N, Kollias S (2004) A snake model for object tracking in natural sequences. Signal processing: Image Communication 19(3):219–238

    Article  Google Scholar 

  38. Udupa J, LeBlanc V, Zhuge Y, Schmidt H, Imielinska C, Hirsch B, Woodburn J (2004) A framework for evaluating image segmentation algorithms. Tech. rep., University of Pennsylvania

    Google Scholar 

  39. Vauhkonen M, Karjalainen P, Kaipio J (1998) A Kalman filter approach applied to the tracking of fast movements of organs boundaries. In: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2, pp. 1048–1051

    Google Scholar 

  40. Wong K, VanMeter J, Fricke S, Maurer C, Cleary K (2004) MRI for modeling of liver and skin respiratory motion. Computer Aided Radiology and Surgery

    Google Scholar 

  41. Yahia-Chereif L, Gilles B, Molet T, Magnenat Thalmann N (2004) Motion capture and visualization of the hip joint with dynamic MRI and optical systems. Computer Animation and Virtuals Worlds 15:377–385

    Article  Google Scholar 

  42. Zeng R, Fessler J, Balter J (2005) Respiratory motion estimation from slowly rotating x-ray projection: Theory and simulation. Medical Physics 32(4):984–991

    Article  Google Scholar 

Download references

Acknowledgments

This work has been partially supported by the Spanish Ministry of Education and Science and the European Union (via ERDF funds) through the research project TIN2007-67474-C03-03, by the Consejería de Innovación, Ciencia y Empresa of the Junta de Andalucía through the research project P06-TIC-01403, and by the University of Jaén through the research project UJA-08-16-02.

Author information

Authors and Affiliations

  1. Departamento de Informática, University of Jaén, Jaén, Spain

    A. Martínez & J. J. Jiménez

Authors
  1. A. Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. J. Jiménez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Martínez .

Editor information

Editors and Affiliations

  1. MIRALab, Université de Genève, route de Drize 7, Genève, 1227, Switzerland

    Nadia Magnenat-Thalmann

  2. Bournemouth Media School, National Centre for Computer Animation, Bournemouth University, Talbot Campus, Poole, Dorset, BH12 5BB, United Kingdom

    Jian J. Zhang

  3. School of Information Technologies, University of Sydney, Sydney, 2006, Australia

    David D. Feng

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag London

About this chapter

Cite this chapter

Martínez, A., Jiménez, J.J. (2009). Tracking Organs Composed of One or Multiple Regions Using Geodesic Active Region Models. In: Magnenat-Thalmann, N., Zhang, J., Feng, D. (eds) Recent Advances in the 3D Physiological Human. Springer, London. https://doi.org/10.1007/978-1-84882-565-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-565-9_3

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-564-2

  • Online ISBN: 978-1-84882-565-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation