Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations and Atlas Location Autocontext

  • Alison O’NeilEmail author
  • Mohammad Dabbah
  • Ian Poole
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


A proof of concept is presented for cross-modality anatomical landmark detection using histograms of unsigned gradient orientations (HUGO) as machine learning image features. This has utility since an existing algorithm trained on data from one modality may be applied to data of a different modality, or data from multiple modalities may be pooled to train one modality-independent algorithm. Landmark detection is performed using a random forest trained on HUGO features and atlas location autocontext features. Three-way cross-modality detection of 20 landmarks is demonstrated in diverse cohorts of CT, MRI T1 and MRI T2 scans of the head. Each cohort is made up of 40 training and 20 test scans, making 180 scans in total. A cross-modality mean landmark error of 5.27 mm is achieved, compared to single-modality error of 4.07 mm.


Anatomical landmarks Random forest Cross-modality Histograms of oriented gradients 


  1. 1.
    Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.H.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Imaging 20(6), 526–534 (2001)CrossRefGoogle Scholar
  2. 2.
    Kohlberger, T., Sofka, M., Zhang, J., Birkbeck, N., Wetzl, J., Kaftan, J., Declerck, J., Zhou, S.K.: Automatic multi-organ segmentation using learning-based segmentation and level set optimization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 338–345. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Toews, M., Zöllei, L., Wells, W.M.: Feature-based alignment of volumetric multi-modal images. In: International Conference on Information Processing in Medical Imaging (IPMI), pp. 25–36 (2013)Google Scholar
  4. 4.
    Heinrich, M.P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F.V., Brady, S.M., Schnabel, J.A.: MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16, 1423–1435 (2012)CrossRefGoogle Scholar
  5. 5.
    Li, Z., Mahapatra, D., Tielbeek, J.A., Stoker, J., van Vliet, L., Vos, F.M.: Image registration based on autocorrelation of local structure. IEEE Trans. Med. Imaging 35(1), 63–75 (2015)CrossRefGoogle Scholar
  6. 6.
    Liu, Y.-Y., Chen, M., Ishikawa, H., Wollstein, G., Schuman, J.S., Rehg, J.M.: Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding. Med. Image Anal. 15, 748–759 (2011)CrossRefGoogle Scholar
  7. 7.
    Smeraldi, F.: Ranklets: orientation selective non-parametric features applied to face detection. In: International Conference on Pattern Recognition (ICPR), vol. 3, pp. 379–382. IEEE Press, New York (2002)Google Scholar
  8. 8.
    Freeman, W., Roth, M.: Orientation histograms for hand gesture recognition. In: IEEE International Conference on Automatic Face and Gesture Recognition (FGR), pp. 296–301. IEEE Press, New York (1995)Google Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893. IEEE Press, New York (2005)Google Scholar
  10. 10.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision (ICCV). IEEE Press, New York (1999)Google Scholar
  11. 11.
    Dabbah, M.A., Murphy, S., Pello, H., Courbon, R., Beveridge, E., Wiseman, S., Wyeth, D., Poole, I.: Detection, location of 127 anatomical landmarks in diverse CT datasets. In: Medical Imaging: Image Processing. Proceedings of the SPIE, vol. 9034, p. 903415 (2014)Google Scholar
  12. 12.
    O’Neil, A., Murphy, S., Poole, I.: Anatomical landmark detection in CT data by learned atlas location autocontext. In: Lambrou, T., Ye, X. (eds.) Proceedings of the 19th Conference on Medical Image Understanding and Analysis, pp. 189–194 (2015)Google Scholar
  13. 13.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Toshiba Medical Visualization SystemsEdinburghUK

Personalised recommendations