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Towards Segmentation of Pedicles on Posteroanterior X-Ray Views of Scoliotic Patients

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4633))

Abstract

The objective of this work is to provide a feasible study to develop an automatic segmentation of pedicles of vertebrae on X-ray images of scoliotic patients, with the ultimate goal of the extraction of high level primitives leading to an accurate 3D spine reconstruction based on stereo-radiographic views.

Our approach relies on coarse and fine parameter free segmentation. First, active contour is performed on a probability score table built from the input pedicle sub-space yielding to a coarse shape. The prior knowledge induced from the latter shape is introduced within a level set model to refine the segmentation, resulting in a fine shape.

For validation purposes, the result obtained by the estimation of the rotation of scoliotic deformations using the resulting fine shape is compared with a gold standard obtained by manual identification by an expert. The results are promising in finding the orientation of scoliotic deformations, and hence can be used for subsequent tools for clinicians.

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References

  1. Antani, S., Long, L.R., Thoma, G.R., Lee, D.J.: Anatomical Shape Representation in Spine X-ray Image. In: Proceedings of IASTED International Conference on Visualization, Imaging and Image Processing, Benalmadena, Spain, September 8-10, 2003, pp. 510–515 (2003)

    Google Scholar 

  2. Xiaoqian, X., Lee, D.J., Antani, S., Long, L.R.: Pre-indexing for fast partial shape matching of vertebrae images Proceedings. In: 19th IEEE International Symposium on Computer-Based Medical Systems, p. 6. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  3. Antani, S., Long, L.R., Thoma, G.R.: Vertebra Shape Classification using MLP for Content-Based Image Retrieval. In: Proceeding of the International Joint Conference on Neural Networks, vol. 1, pp. 160–165 (2003)

    Google Scholar 

  4. Koompairojn, S., Hua, K.A., Bhadrakom, C.: Automatic classification system for lumbar spine X-ray images. In: Proceedings 19th IEEE International Symposium on Computer-Based Medical Systems, p. 6 (2006)

    Google Scholar 

  5. Duong, L., Cheriet, F., Labelle, H.: Towards an Automatic Classification of Spinal Curves from X-Ray Images. Stud. Health Technol. Inform. 123, 419–424 (2006)

    Google Scholar 

  6. Caselles, V., Catté, F., Coll, B., Dibos, F.: A geometric model for activr contours in image processing. Numerische Mathematik 66(1), 1–31 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  7. Paragios, N., Chen, Y., Faugeras, O.: Handbook of Mathematical Models in Computer Vision. Springer, New York (2006)

    Google Scholar 

  8. Cremers, D., Tischhäuser, F., Weickert, J., Schnörr, C.: Diffusion Snakes: Introducing statistical shape knowledge into the Mumford-Shah functional. International Journal on Computer Vision 50(3), 295–313 (2002)

    Article  MATH  Google Scholar 

  9. Hong, B.W., Prados, E., Soatto, S., Vese, L.: Shape Representation based on Integral Kernels: Application To Image Matching and Segmentation, Computer vision and pattern Recognition. In: IEEE Computer Society Conference on, June 2006, vol. 1, pp. 833–840 (2006)

    Google Scholar 

  10. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  11. Rousson, M., Paragios, N.: Shape Prior for Level Set Representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 77–92. Springer, Heidelberg (2002)

    Google Scholar 

  12. O’Brien, M.F., Kuklo, T.R., Blanke, K.M., Lenke, L.G.: Editors-in-Chief. Radiographic Measurement Manual. Spinal Deformity Study Group (SDSG). Medtronic Sofamor Danek, Memphis, Tennessee, 110 pages (2004)

    Google Scholar 

  13. Otsu, N.: A Threshold Selection Method from Grey-Level Histograms. IEEE Trans. Syst., Man, Cybern. 9, 62–66 (1978)

    Google Scholar 

  14. Stokes IAF (chair): Scoliosis Research Society Working Group on 3-d terminology of spinal deformity Three-dimensional terminology of spinal deformity. Spine 19, 236–248 (1994)

    Google Scholar 

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Mohamed Kamel Aurélio Campilho

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© 2007 Springer-Verlag Berlin Heidelberg

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Doré, V., Duong, L., Cheriet, F., Cheriet, M. (2007). Towards Segmentation of Pedicles on Posteroanterior X-Ray Views of Scoliotic Patients. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_91

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  • DOI: https://doi.org/10.1007/978-3-540-74260-9_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74258-6

  • Online ISBN: 978-3-540-74260-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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