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Registration of Lung Surface Proximity for Assessment of Pleural Thickenings

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Bildverarbeitung für die Medizin 2012

Part of the book series: Informatik aktuell ((INFORMAT))

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Abstract

Follow-up assessment of pleural thickenings requires the comparison of information from different points in time. The investigated image regions must be precisely registered to acquire this information. Since the thickenings’ growth is the target value, this growth should not be compensated by the registration process. We therefore present a nonrigid registration method, which preserves the shape of the thickenings. The deformation of the volume image is carried out using B-splines. With focus on the image regions located around the lung surface, an efficient way of calculating corresponding points combined with the reuse of information from different scale levels leads to the non-rigid registration, which can be performed within a short computation time.

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References

  1. Ochsmann E, Carl T, Brand P, et al. Inter-reader variability in chest radiography and HRCT for the early detection of asbestos-related lung and pleural abnormalities in a cohort of 636 asbestos-exposed subjects. Int Arch Occup Environ Health. 2010;83:39–46.

    Article  Google Scholar 

  2. Rueckert D, Sonoda LI, Hayes C, et al. Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans Med Imaging. 1999;18:712–21.

    Article  Google Scholar 

  3. Loeckx D, Slagmolen P, Maes F, et al. Nonrigid image registration using conditional mutual information. IEEE Trans Med Imaging. 2007;29(1):725–37.

    Google Scholar 

  4. Chui H, Win L, Schultz R, et al. A unified non-rigid feature registration method for brain mapping. Med Image Anal. 2003;7(2):113–30.

    Article  Google Scholar 

  5. Kwon D, Yun ID, Lee KH, et al. Efficient feature-based nonrigid registration of multiphase liver CT volumes. In: BMVC; 2008.

    Google Scholar 

  6. Chaisaowong K, Bross B, Knepper A, et al. Detection and follow-up assessment of pleural thickenings from 3D CT data. In: ECTI; 2008. p. 489–92.

    Google Scholar 

  7. Faltin P, Chaisaowong K, Kraus T, et al. Markov-Gibbs model based registration of CT lung images using subsampling for the follow-up assessment of pleural thickenings. In: IEEE ICIP; 2011. p. 2229–32.

    Google Scholar 

  8. Lee S, Wolberg G, Shin SY. Scattered data interpolation with multilevel B-splines. IEEE Trans Vis Comput Graph. 1997;3:228–244.

    Article  Google Scholar 

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Correspondence to Peter Faltin .

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© 2012 Springer-Verlag Berlin Heidelberg

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Faltin, P., Chaisaowong, K., Kraus, T., Aach, T. (2012). Registration of Lung Surface Proximity for Assessment of Pleural Thickenings. In: Tolxdorff, T., Deserno, T., Handels, H., Meinzer, HP. (eds) Bildverarbeitung für die Medizin 2012. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28502-8_38

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