Abstract
Recent work has shown that using Random Forests (RFs) to vote for the optimal position of model feature points leads to robust and accurate shape model matching. This paper applies RF regression-voting as part of a fully automatic shape model matching (FASMM) system to three different radiograph segmentation problems: the proximal femur, the bones of the knee joint and the joints of the hand. We investigate why this approach works so well and demonstrate that the performance comes from a combination of three properties: (i) The integration of votes from multiple regions around the model point. (ii) The combination of multiple independent votes from each tree. (iii) The use of a coarse to fine strategy. We show that each property can improve performance, and that the best performance comes from using all three. We demonstrate that FASMM based on RF regression-voting generalises well across application areas, achieving state of the art performance in each of the three segmentation problems. This FASMM system provides an accurate and time-efficient way for the segmentation of bony structures in radiographs.
Chapter PDF
Similar content being viewed by others
Keywords
References
Breiman, L.: Random Forests. Machine Learning 45, 5–32 (2001)
Cootes, T.F., Ionita, M.C., Lindner, C., Sauer, P.: Robust and Accurate Shape Model Fitting using Random Forest Regression Voting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 278–291. Springer, Heidelberg (2012)
Cootes, T.F., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)
Donner, R., Menze, B.H., Bischof, H., Langs, G.: Fast anatomical structure localization using top-down image patch regression. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 133–141. Springer, Heidelberg (2013)
Gall, J., Lempitsky, V.: Class-specific Hough forests for object detection. In: CVPR, pp. 1022–1029. IEEE Press (2009)
Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient Regression of General-Activity Human Poses from Depth Images. In: ICCV, pp. 415–422. IEEE Press (2011)
Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012)
Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A.: Neighbourhood Approximation Forests. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 75–82. Springer, Heidelberg (2012)
Lindner, C., Thiagarajah, S., Wilkinson, J.M., Wallis, G.A., Cootes, T.F.: Accurate fully automatic femur segmentation in pelvic radiographs using regression voting. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 353–360. Springer, Heidelberg (2012)
Seise, M., McKenna, S., Ricketts, I., Wigderowitz, C.: Learning active shape models for bifurcating contours. IEEE Trans. on Medical Imaging 26(5), 666–677 (2007)
Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: CVPR, pp. 511–518. IEEE Press (2001)
Author information
Authors and Affiliations
Consortia
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lindner, C., Thiagarajah, S., Wilkinson, J.M., arcOGEN Consortium., Wallis, G.A., Cootes, T.F. (2013). Accurate Bone Segmentation in 2D Radiographs Using Fully Automatic Shape Model Matching Based On Regression-Voting. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-40763-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40762-8
Online ISBN: 978-3-642-40763-5
eBook Packages: Computer ScienceComputer Science (R0)