Biomedical Imaging: A Computer Vision Perspective

  • Xiaoyi Jiang
  • Mohammad Dawood
  • Fabian Gigengack
  • Benjamin Risse
  • Sönke Schmid
  • Daniel Tenbrinck
  • Klaus Schäfers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8047)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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. 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)CrossRefGoogle Scholar
  5. 5.
    Bruhn, A.: Variational Optic Flow Computation – Accurate Modelling and Efficient Numerics. Ph.D. thesis, University of Saarland (2006)Google Scholar
  6. 6.
    Burkard, R., Dell’Amico, M., Martello, S.: Assignment Problems. Society for Industrial Mathematics (2009)Google Scholar
  7. 7.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)MATHCrossRefGoogle Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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. 14.
    Dawood, M., Jiang, X., Schäfers, K. (eds.): Correction Techniques in Emission Tomographic Imaging. CRC Press (2012)Google Scholar
  15. 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)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 18.
    Falcão, A.X., Udupa, J.K.: A 3D generalization of user-steered live-wire segmentation. Medical Image Analysis 4(4), 389–402 (2000)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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)CrossRefGoogle Scholar
  21. 21.
    Fischer, B., Modersitzki, J.: Ill-posed medicine - an introduction to image registration. Inverse Problems 24(3), 034008 (2008)MathSciNetCrossRefGoogle Scholar
  22. 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. 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. 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)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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. 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. 28.
    Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: A review. Medical Image Analysis 13(4), 543–563 (2009)CrossRefGoogle Scholar
  29. 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. 30.
    Jiang, X., Große, A., Rothaus, K.: Interactive segmentation of non-star-shaped contours by dynamic programming. Pattern Recognition 44(9), 2008–2016 (2011)CrossRefGoogle Scholar
  31. 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. 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)CrossRefGoogle Scholar
  33. 33.
    Li, L., Yang, Y.: Optical flow estimation for a periodic image sequence. IEEE Trans. on Image Processing 19(1), 1–10 (2010)CrossRefGoogle Scholar
  34. 34.
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 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)CrossRefGoogle Scholar
  37. 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. 38.
    Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)MathSciNetMATHCrossRefGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 40.
    Qiu, M.: Computing optical flow based on the mass-conserving assumption. In: Proc. of ICPR, pp. 7041–7044 (2000)Google Scholar
  41. 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. 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/s10851-013-0419-6Google Scholar
  43. 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. 44.
    Schunck, B.: The motion constraint equation for optical flow. In: Proc. of ICPR, pp. 20–22 (1984)Google Scholar
  45. 45.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, 3rd edn. (2007)Google Scholar
  46. 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)CrossRefGoogle Scholar
  47. 47.
    Sun, C., Pallottino, S.: Circular shortest path in images. Pattern Recognition 36(3), 709–719 (2003)CrossRefGoogle Scholar
  48. 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. 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-zGoogle Scholar
  50. 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. 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. 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. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaoyi Jiang
    • 1
    • 2
    • 3
  • Mohammad Dawood
    • 1
    • 2
  • Fabian Gigengack
    • 1
    • 2
  • Benjamin Risse
    • 1
    • 4
  • Sönke Schmid
    • 1
    • 2
    • 3
  • Daniel Tenbrinck
    • 1
    • 2
  • Klaus Schäfers
    • 2
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity of MünsterGermany
  2. 2.European Institute for Molecular Imaging (EIMI)University of MünsterGermany
  3. 3.Cluster of Excellence EXC 1003, Cells in Motion, CiMMünsterGermany
  4. 4.Department of Neuro and Behavioral BiologyUniversity of MünsterGermany

Personalised recommendations