Skip to main content
SpringerLink
Log in

Biomedical Imaging: A Computer Vision Perspective

  • Conference paper
Computer Analysis of Images and Patterns (CAIP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8047))

Included in the following conference series:

Abstract

Many computer vision algorithms have been successfully adapted and applied to biomedical imaging applications. However, biomedical computer vision is far beyond being only an application field. Indeed, it is a wide field with huge potential for developing novel concepts and algorithms and can be seen as a driving force for computer vision research. To emphasize this view of biomedical computer vision we consider a variety of important topics of biomedical imaging in this paper and exemplarily discuss some challenges, the related concepts, techniques, and algorithms.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ardekani, R., Biyani, A., Dalton, J., Saltz, J., Arbeitman, M., Tower, J., Nuzhdin, S., Tavare, S.: Three-dimensional tracking and behaviour monitoring of multiple fruit flies. J. R. Soc. Interface 10(78), 20120547 (2013)

    Article  Google Scholar 

  2. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 92(1), 1–31 (2011)

    Article  Google Scholar 

  3. Béréziat, D., Herlin, I., Younes, L.: A generalized optical flow constraint and its physical interpretation. In: Proc. of CVPR, pp. 487–492 (2000)

    Google Scholar 

  4. Bimbo, A.D., Nesi, P., Sanz, J.L.C.: Optical flow computation using extended constraints. IEEE Trans. on Image Processing 5(5), 720–739 (1996)

    Article  Google Scholar 

  5. Bruhn, A.: Variational Optic Flow Computation – Accurate Modelling and Efficient Numerics. Ph.D. thesis, University of Saarland (2006)

    Google Scholar 

  6. Burkard, R., Dell’Amico, M., Martello, S.: Assignment Problems. Society for Industrial Mathematics (2009)

    Google Scholar 

  7. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  8. Cheng, D.C., Jiang, X.: Detections of arterial wall in sonographic artery images using dual dynamic programming. IEEE Trans. on Information Technology in Biomedicine 12(6), 792–799 (2008)

    Article  Google Scholar 

  9. Chesnaud, C., Réfrégier, P., Boulet, V.: Statistical region snake-based segmentation adapted to different physical noise models. IEEE Trans. on Pattern Anaysis and Machine Intelligence 21(11), 1145–1157 (1999)

    Article  Google Scholar 

  10. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)

    Article  Google Scholar 

  11. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)

    Article  Google Scholar 

  12. Corpetti, T., Heitz, D., Arroyo, G., Memin, E., Santa-Cruz, A.: Fluid experimental flow estimation based on an optical-flow scheme. Experiments in Fluids 40(1), 80–97 (2006)

    Article  Google Scholar 

  13. Dawood, M., Gigengack, F., Jiang, X., Schäfers, K.: A mass conservation-based optical flow method for cardiac motion correction in 3D-PET. Medical Physics 40(1), 012505 (2013)

    Google Scholar 

  14. Dawood, M., Jiang, X., Schäfers, K. (eds.): Correction Techniques in Emission Tomographic Imaging. CRC Press (2012)

    Google Scholar 

  15. Dawood, M., Büther, F., Jiang, X., Schäfers, K.P.: Respiratory motion correction in 3-D PET data with advanced optical flow algorithms. IEEE Trans. on Medical Imaging 27(8), 1164–1175 (2008)

    Article  Google Scholar 

  16. Dawood, M., Büther, F., Stegger, L., Jiang, X., Schober, O., Schäfers, M., Schäfers, K.P.: Optimal number of respiratory gates in positron emission tomography: A cardiac patient study. Medical Physics 36(5), 1775–1784 (2009)

    Article  Google Scholar 

  17. Dawood, M., Kösters, T., Fieseler, M., Büther, F., Jiang, X., Wübbeling, F., Schäfers, K.P.: Motion correction in respiratory gated cardiac PET/CT using multi-scale optical flow. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 155–162. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Falcão, A.X., Udupa, J.K.: A 3D generalization of user-steered live-wire segmentation. Medical Image Analysis 4(4), 389–402 (2000)

    Article  Google Scholar 

  19. Falcão, A.X., Udupa, J.K., Miyazawa, F.K.: An ultra-fast user-steered image segementation paradigm: Live-wire-on-the-fly. IEEE Trans. on Medical Imaging 19(1), 55–62 (2000)

    Article  Google Scholar 

  20. Falcão, A.X., Udupa, J.K., Samarasekera, S., Sharma, S., Hirsch, B.E., de Alencar Lotufo, R.: User-steered image segmentation paradigms: Live wire and live lane. Graphical Models and Image Processing 60(4), 233–260 (1998)

    Article  Google Scholar 

  21. Fischer, B., Modersitzki, J.: Ill-posed medicine - an introduction to image registration. Inverse Problems 24(3), 034008 (2008)

    Article  MathSciNet  Google Scholar 

  22. Fleet, D., Weiss, Y.: Optical flow estimation. In: Paragios, N., Chen, Y., Fauregas, O. (eds.) The Handbook of Mathematical Models in Computer Vision, pp. 241–260. Springer (2005)

    Google Scholar 

  23. Gigengack, F.: Mass-Preserving Motion Correction and Multimodal Image Segementation in Positron Emission Tomography. Ph.D. thesis, University of Münster (2012)

    Google Scholar 

  24. Gigengack, F., Ruthotto, L., Burger, M., Wolters, C.H., Jiang, X., Schäfers, K.P.: Motion correction in dual gated cardiac PET using mass-preserving image registration. IEEE Trans. on Medical Imaging 31(3), 698–712 (2012)

    Article  Google Scholar 

  25. Gigengack, F., Ruthotto, L., Jiang, X., Modersitzki, J., Burger, M., Hermann, S., Schäfers, K.P.: Atlas-based whole-body PET-CT segmentation using a passive contour distance. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 82–92. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  26. von Gioi, R.G., Monasse, P., Morel, J.M., Tang, Z.: Towards high-precision lens distortion correction. In: Proc. of ICIP, pp. 4237–4240 (2010)

    Google Scholar 

  27. von Gioi, R.G., Monasse, P., Morel, J.M., Tang, Z.: Lens distortion correction with a calibration harp. In: Proc. of ICIP, pp. 617–620 (2011)

    Google Scholar 

  28. Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis 13(4), 543–563 (2009)

    Article  Google Scholar 

  29. Jiang, X., Tenbrinck, D.: Region based contour detection by dynamic programming. In: Hancock, E., Smith, W., Wilson, R., Bors, A. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 152–159. Springer, Heidelberg (2013)

    Google Scholar 

  30. Jiang, X., Große, A., Rothaus, K.: Interactive segmentation of non-star-shaped contours by dynamic programming. Pattern Recognition 44(9), 2008–2016 (2011)

    Article  Google Scholar 

  31. Khurana, S., Atkinson, W.L.N.: Image enhancement for tracking the translucent larvae of drosophila melanogaster. PLoS ONE 5(12), e15259 (2010)

    Google Scholar 

  32. Li, K., Wu, X., Chen, D., Sonka, M.: Optimal surface segmentation in volumetric images - a graph-theoretic approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(1), 119–134 (2006)

    Article  Google Scholar 

  33. Li, L., Yang, Y.: Optical flow estimation for a periodic image sequence. IEEE Trans. on Image Processing 19(1), 1–10 (2010)

    Article  Google Scholar 

  34. Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)

    Article  Google Scholar 

  35. Malon, C., Cosatto, E.: Dynamic radial contour extraction by splitting homogeneous areas. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part I. LNCS, vol. 6854, pp. 269–277. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  36. Martin, P., Réfrégier, P., Goudail, F., Guérault, F.: Influence of the noise model on level set active contour segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(6), 799–803 (2004)

    Article  Google Scholar 

  37. Mortensen, E., Morse, B., Barrett, W.: Adaptive boundary detection using ‘live-wire’ two-dimensional dynamic programming. In: IEEE Proc. Computers in Cardiology, pp. 635–638 (1992)

    Google Scholar 

  38. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  39. Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual information based registration of medical images: A survey. IEEE Trans. on Medical Imaging 22(8), 986–1004 (2003)

    Article  Google Scholar 

  40. Qiu, M.: Computing optical flow based on the mass-conserving assumption. In: Proc. of ICPR, pp. 7041–7044 (2000)

    Google Scholar 

  41. Risse, B., Thomas, S., Otto, N., Löpmeier, T., Valkov, D., Jiang, X., Klämbt, C.: FIM, a novel FTIR-based imaging method for high throughput locomotion analysis. PLoS ONE 8(1), e53963 (2013)

    Google Scholar 

  42. Sawatzky, A., Tenbrinck, D., Jiang, X., Burger, M.: A variational framework for region-based segmentation incorporating physical noise models. Journal of Mathematical Imaging and Vision (2013), doi:10.1007/s10851-013-0419-6

    Google Scholar 

  43. Schmid, S., Jiang, X., Schäfers, K.: High-precision lens distortion correction using smoothed thin plate splines. In: Hancock, E., Smith, W., Wilson, R., Bors, A. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 432–439. Springer, Heidelberg (2013)

    Google Scholar 

  44. Schunck, B.: The motion constraint equation for optical flow. In: Proc. of ICPR, pp. 20–22 (1984)

    Google Scholar 

  45. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, 3rd edn. (2007)

    Google Scholar 

  46. Sun, C., Appleton, B.: Multiple paths extraction in images using a constrained expanded trellis. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(12), 1923–1933 (2005)

    Article  Google Scholar 

  47. Sun, C., Pallottino, S.: Circular shortest path in images. Pattern Recognition 36(3), 709–719 (2003)

    Article  Google Scholar 

  48. Tenbrinck, D., Jiang, X.: Discriminant analysis based level set segmentation for ultrasound imaging. In: Hancock, E., Smith, W., Wilson, R., Bors, A. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 144–151. Springer, Heidelberg (2013)

    Google Scholar 

  49. Tenbrinck, D., Schmid, S., Jiang, X., Schäfers, K., Stypmann, J.: Histogram-based optical flow for motion estimation in ultrasound imaging. Journal of Mathematical Imaging and Vision (2013), doi:10.1007/s10851-012-0398-z

    Google Scholar 

  50. Tenbrinck, D., Sawatzky, A., Jiang, X., Burger, M., Haffner, W., Willems, P., Paul, M., Stypmann, J.: Impact of physical noise modeling on image segmentation in echocardiography. In: Proc. of Eurographics Workshop on Visual Computing for Biomedicine, pp. 33–40 (2012)

    Google Scholar 

  51. Udupa, J., Samarasekera, S., Barrett, W.: Boundary detection via dynamic programming. In: Visualization in Biomedical Computing 1992, pp. 33–39 (1992)

    Google Scholar 

  52. Yan, H., Gigengack, F., Jiang, X., Schäfers, K.: Super-resolution in cardiac PET using mass-preserving image registration. In: Proc. of ICIP (2013)

    Google Scholar 

  53. Yu, M., Huang, Q., Jin, R., Song, E., Liu, H., Hung, C.C.: A novel segmentation method for convex lesions based on dynamic programming with local intra-class variance. In: Proc. of ACM Symposium on Applied Computing, pp. 39–44 (2012)

    Google Scholar 

  54. Zou, D., Zhao, Q., Wu, H.S., Chen, Y.Q.: Reconstructing 3d motion trajectories of particle swarms by global correspondence selection. In: Proc. of ICCV, pp. 1578–1585 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Germany

    Xiaoyi Jiang, Mohammad Dawood, Fabian Gigengack, Benjamin Risse, Sönke Schmid & Daniel Tenbrinck

  2. European Institute for Molecular Imaging (EIMI), University of Münster, Germany

    Xiaoyi Jiang, Mohammad Dawood, Fabian Gigengack, Sönke Schmid, Daniel Tenbrinck & Klaus Schäfers

  3. Cluster of Excellence EXC 1003, Cells in Motion, CiM, Münster, Germany

    Xiaoyi Jiang, Sönke Schmid & Klaus Schäfers

  4. Department of Neuro and Behavioral Biology, University of Münster, Germany

    Benjamin Risse

Authors
  1. Xiaoyi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Dawood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fabian Gigengack
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benjamin Risse
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sönke Schmid
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel Tenbrinck
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Klaus Schäfers
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Richard Wilson , Edwin Hancock , Adrian Bors  & William Smith , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jiang, X. et al. (2013). Biomedical Imaging: A Computer Vision Perspective. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40261-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40260-9

  • Online ISBN: 978-3-642-40261-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation