Advertisement

Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points

  • Alberto GomezEmail author
  • Kanwal Bhatia
  • Sarjana Tharin
  • James Housden
  • Nicolas Toussaint
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)

Abstract

We propose a fast feature-based rigid registration framework with a novel feature saliency detection technique. The method works by automatically classifying candidate image points as salient or non-salient using a support vector machine trained on points which have previously driven successful registrations. Resulting candidate salient points are used for symmetric matching based on local descriptor similarity and followed by RANSAC outlier rejection to obtain the final transform. The proposed registration framework was applied to 3D real-time fetal ultrasound images, thus covering the entire fetal anatomy for extended FoV imaging. Our method was applied to data from 5 patients, and compared to a conventional saliency point detection method (SIFT) in terms of computational time, quality of the point detection and registration accuracy. Our method achieved similar accuracy and similar saliency detection quality in \(<5\%\) the detection time, showing promising capabilities towards real-time whole-body fetal ultrasound imaging.

References

  1. 1.
    Banerjee, J., Klink, C., Peters, E.D., Niessen, W.J., Moelker, A., van Walsum, T.: Fast and robust 3D ultrasound registration - Block and game theoretic matching. Med. Image Anal. 20(1), 173–183 (2015)CrossRefGoogle Scholar
  2. 2.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Grau, V., Becher, H., Noble, J.A.: Registration of multiview real-time 3-D echocardiographic sequences. IEEE Trans. Med. Imaging 26(9), 1154–1165 (2007)CrossRefGoogle Scholar
  4. 4.
    Kacem, Y., Cannie, M.M., Kadji, C., Dobrescu, O., Lo Zito, L., Ziane, S., Strizek, B., Evrard, A.-S., Gubana, F., Gucciardo, L., Staelens, R., Jani, J.C.: Fetal weight estimation: Comparison of two-dimensional US and MR imaging assessments. Radiology 267(3), 902–910 (2013)CrossRefGoogle Scholar
  5. 5.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Ni, D., Qu, Y., Yang, X., Chui, Y.P., Wong, T.-T., Ho, S.S.M., Heng, P.A.: Volumetric ultrasound panorama based on 3D SIFT. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 52–60. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85990-1_7 CrossRefGoogle Scholar
  7. 7.
    Oktay, O., Schuh, A., Rajchl, M., Keraudren, K., Gomez, A., Heinrich, M.P., Penney, G., Rueckert, D.: Structured decision forests for multi-modal ultrasound image registration. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 363–371. Springer, Cham (2015). doi: 10.1007/978-3-319-24571-3_44 CrossRefGoogle Scholar
  8. 8.
    Ourselin, S., Roche, A., Prima, S., Ayache, N.: Block matching: A general framework to improve robustness of rigid registration of medical images. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 557–566. Springer, Heidelberg (2000). doi: 10.1007/978-3-540-40899-4_57 CrossRefGoogle Scholar
  9. 9.
    Schneider, R.J., Perrin, D.P., Vasilyev, N.V., Marx, G.R., Del Nido, P.J., Howe, R.D.: Real-time image-based rigid registration of three-dimensional ultrasound. Med. Image Anal. 16(2), 402–414 (2012)CrossRefGoogle Scholar
  10. 10.
    Wachinger, C., Navab, N.: Simultaneous registration of multiple images: similarity metrics and efficient optimization. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1221–1233 (2013)CrossRefGoogle Scholar
  11. 11.
    Wachinger, C., Wein, W., Navab, N.: Three-dimensional ultrasound mosaicing. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4792, pp. 327–335. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-75759-7_40 CrossRefGoogle Scholar
  12. 12.
    Yao, C., Simpson, J.M., Schaeffter, T., Penney, G.P.: Multi-view 3D echocardiography compounding based on feature consistency. Phys. Med. Biol. 56(18), 6109–6128 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alberto Gomez
    • 1
    Email author
  • Kanwal Bhatia
    • 1
  • Sarjana Tharin
    • 1
  • James Housden
    • 1
  • Nicolas Toussaint
    • 1
  • Julia A. Schnabel
    • 1
  1. 1.Department of Biomedical EngineeringKing’s College LondonLondonUK

Personalised recommendations