Abstract
We present a flexible and general framework to iteratively solve quadratic energy problems on a non uniform grid, targeted at ultrasound imaging. Therefore, we model input samples as the nodes of an irregular directed graph, and define energies according to the application by setting weights to the edges. To solve the energy, we derive an effective optimization scheme, which avoids both the explicit computation of a linear system, as well as the compounding of the input data on a regular grid. The framework is validated in the context of 3D ultrasound signal loss estimation with the goal of providing an uncertainty estimate for each 3D data sample. Qualitative and quantitative results for 5 subjects and two target regions, namely US of the bone and the carotid artery, show the benefits of our approach, yielding continuous loss estimates.
This work was partially supported by the EU 7th Framework, No. 270460 (ACTIVE), and the Bavarian state program Leitprojekte Medizintechnik (BayMED).
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References
Grady, L.: Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)
Grewenig, S., Weickert, J., Schroers, C., Bruhn, A.: Cyclic schemes for pde-based image analysis. Tech. rep., Technical Report 327, Department of Mathematics, Saarland University, Saarbrücken, Germany (2013)
Karamalis, A., Wein, W., Klein, T., Navab, N.: Ultrasound confidence maps using random walks. Medical Image Analysis 16(6), 1101–1112 (2012)
Prager, R., Gee, A., Treece, G., Berman, L.: Freehand 3D ultrasound without voxels: volume measurement and visualisation using the stradx system. Ultrasonics 40(18), 109–115 (2002)
Singaraju, D., Grady, L., Vidal, R.: Interactive image segmentation via minimization of quadratic energies on directed graphs. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (June 2008)
Solberg, O.V., Lindseth, F., Torp, H., Blake, R.E., Nagelhus Hernes, T.A.: Freehand 3d ultrasound reconstruction algorithmsa review. Ultrasound in Medicine and Biology 33(7), 991–1009 (2007)
Weickert, J., Romeny, B.M.T.H., Viergever, M.A.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing 7(3), 398–410 (1998)
Yu, S.T.Y., Acton: Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 11(11), 1260–1270 (2002)
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Hennersperger, C., Mateus, D., Baust, M., Navab, N. (2014). A Quadratic Energy Minimization Framework for Signal Loss Estimation from Arbitrarily Sampled Ultrasound Data. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_47
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DOI: https://doi.org/10.1007/978-3-319-10470-6_47
Publisher Name: Springer, Cham
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