Abstract
We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that represents the lumbar intervertebral discs and we use an exact inference algorithm to localize the discs. Our main contributions are the employment of the SVM with the PHOG based descriptor which is robust against variations of the discs and a graphical model that reflects the linear nature of the vertebral column. Our inference algorithm runs in polynomial time and produces globally optimal results. The developed system is validated on a real spine MRI dataset and the final localization results are favorable compared to the results reported in the literature.
Chapter PDF
Similar content being viewed by others
References
Alomari, R.S., Corso, J.J., Chaudhary, V.: Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model. IEEE Trans. Med. Imaging 30, 1–10 (2011)
Bhole, C., Kompalli, S., Chaudhary, V.: Context-sensitive Labeling of Spinal Structures in MRI Images. In: SPIE Medical Imaging (2009)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, Heidelberg (2007)
Bosch, A., Zisserman, A., Munoz, X.: Representing Shape with a Spatial Pyramid Kernel. In: Proceedings of the International Conference on Image and Video Retrieval (2007)
Carballido-Gamio, J., Belongie, S.J., Majumdar, S.: Normalized Cuts in 3-D for Spinal MRI Segmentation. IEEE Trans. Med. Imaging 23, 36–44 (2004)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 886–893 (June 2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: IEEE Conference on Computer Vision and Pattern Recognition (2006)
Lepetit, V., Fua, P.: Keypoint Recognition Using Randomized Trees. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1465–1479 (2006)
Peng, Z., Zhong, J., Wee, W., Huei Lee, J.: Automated Vertebra Detection and Segmentation from the Whole Spine MR Images. In: Conf. Proc. IEEE Engineering in Medicine and Biology Society (2005)
Platt, J.C.: Advances in Kernel Methods, pp. 185–208. MIT Press, Cambridge (1999)
Platt, J.C.: Advances in Large Margin Classifiers, pp. 61–74 (1999)
Porikli, F.: Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 829–836 (2005)
Schmidt, S., Bergtholdt, M., Dries, S., Schnorr, C.: Spine Detection and Labeling Using a Parts-Based Graphical Model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (2007)
Seifert, S., Wachter, I., Schmelzle, G., Dillmann, R.: A Knowledge-based Approach to Soft Tissue Reconstruction of the Cervical Spine. IEEE Trans. Med. Imaging 28(4), 494–507 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oktay, A.B., Akgul, Y.S. (2011). Localization of the Lumbar Discs Using Machine Learning and Exact Probabilistic Inference. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6893. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23626-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-23626-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23625-9
Online ISBN: 978-3-642-23626-6
eBook Packages: Computer ScienceComputer Science (R0)