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Robust Surface Registration Using a Gaussian-Weighted Distance Map in PET-CT Brain Images

  • Ho Lee
  • Helen Hong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

In this paper, we propose a robust surface registration using a Gaussian-weighted distance map for PET-CT brain fusion. Our method is composed of three steps. First, we segment the head using the inverse region growing and remove the non-head regions segmented with the head using the region growing-based labeling in PET and CT images, respectively. The feature points of the head are then extracted using sharpening filter. Second, a Gaussian-weighted distance map is generated from the feature points of CT images to lead our similarity measure to robust convergence on the optimal location. Third, weighted cross-correlation measures the similarities between the feature points extracted from PET images and the Gaussian-weighted distance map of CT images. In our experiments, we use software phantom and clinical datasets for evaluating our method with the aspect of visual inspection, accuracy, robustness, and computation time. Experimental results show that our method is more accurate and robust than the conventional ones.

Keywords

Positron Emission Tomography Compute Tomography Image Feature Point Positron Emission Tomography Image Clinical Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  2. 2.
    Hongjian, J., Richard, R.A., Holton K.S.: New approach to 3-D registration of multimodality medical images by surface matching. In: Proc. SPIE, vol. 1808, pp. 196–213.Google Scholar
  3. 3.
    Maintz, J.B.A., van den Elsen, P.A., Viergever, M.A.: Comparison of edge-based and ridge-based registration of CT and MR brain images. Medical Image Analysis 1(2), 151–161 (1996)CrossRefGoogle Scholar
  4. 4.
    Maes, F., Collignon, A., Marchal, G., Suetens, P.: Multimodality Image Registration by maximization of Mutual Information. IEEE Transaction on Medical Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  5. 5.
    Hsu, L.Y., Loew, M.H.: Fully automatic 3D feature-based registration of multi-modality medical images. Image and Vision Computing 19, 75–85 (2001)CrossRefGoogle Scholar
  6. 6.
    Firle, E.A., Wesarg, S., Dold, C.: Fast CT/PET registration based on partial volume matching. International Congress Series 1268, 1440–1445 (2004)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, R.G., Woods, R.E.: Digital Image Processing, 1st edn. (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ho Lee
    • 1
  • Helen Hong
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceSeoul National University 
  2. 2.School of Electrical Engineering and Computer Science, BK21: Information TechnologySeoul National UniversitySeoulKorea

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