Biomedical Engineering Reference
In-Depth Information
The semi-implicit method we propose is well founded mathematically when the
time-discretized problems are dealt with and it represents a feasible alternative to gaus-
sian denoising for low SNR MR images. Further study is undoubtedly necessary in
order to make automatic the choice of the parameters in real medical images. Other
possibilities, such as Inverse scaling, which makes the parameter estimation less crucial
and provide contrast enhanced images shall also be explored.
Acknowledgements. This work was supported by project TEC2009-14587-C03-03 of
the Spanish Ministry of Science. Also we thank to Mrs. Eva Alfayate, MR-scanner
technician of the Fundacion Reina Sofıa, for her professional and kindly collaboration.
References
1. Henkelman, R.M.: Measurement of signal intensities in the presence of noise in MR images.
Med. Phys. 12(2), 232-233 (1985)
2. Gudbjartsson, H., Patz, S.: The Rician distribution of noisy MRI data. J. of Magn. Reson.
Med. 34(6), 910-914 (1995)
3. Sijbers, J., Den Dekker, A.J., Van Audekerke, J., Verhoye, M., Van Dyck, D.: Estimation of
the noise in magnitude MR images. Magn. Reson. Imaging 16(1), 87-98 (1998)
4. Martın, A., Garamendi, J.F., Schiavi, E.: Iterated Rician Denoising. In: Proceedings of the
International Conference on Image Processing, Computer Vision and Pattern Recognition,
IPCV 2011, pp. 959-963. CSREA Press, Las Vegas (2011)
5. Getreuer, P., Tong, M., Vese, L.A.: A Variational Model for the Restoration of MR Images
Corrupted by Blur and Rician Noise. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Wang,
S., Kyungnam, K., Benes, B., Moreland, K., Borst, C., DiVerdi, S., Yi-Jen, C., Ming, J. (eds.)
ISVC 2011, Part I. LNCS, vol. 6938, pp. 686-698. Springer, Heidelberg (2011)
6. Basu, S., Fletcher, T., Whitaker, R.T.: Rician Noise Removal in Diffusion Tensor MRI. In:
Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part I. LNCS, vol. 4190, pp.
117-125. Springer, Heidelberg (2006)
7. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms.
Physica D 60, 259-268 (1992)
8. Chambolle, A.: An algorithm for Total variation Minimization and Applications. J. of Math-
ematical Imaging and Vision 20, 89-97 (2004)
9. Ambrosio, L., Fusco, N., Pallara, D.: Functions of Bounded Variation and free discontinuity
problems. Oxford Mathematical Monographs. The Clarendon Press, Oxford University (2000)
10. Lassey, K.R.: On the Computation of Certain Integrals Containing the Modified Bessel Func-
tion I 0 ( ξ ) . J. of Mathematics of Computation 39(160), 625-637 (1982)
11. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans-
actions on PAMI 12(7), 629-639 (1990)
12. Nikolova, M., Esedoglu, S., Chan, T.F.: Algorithms for Finding Global Minimizers of Image
Segmentation and Denoising Models. SIAM Journal of Applied Mathematics 66(5), 1632-
1648 (2006)
13. Aubert-Broche, B., Griffin, M., Pike, G., Evans, A., Collins, D.: Twenty New Digital Brain
Phantoms for Creation of Validation Image Data Bases. IEEE Transactions on Medical Imag-
ing 24(11), 1410-1416 (2006)
14. Jones, D.K., Horsfield, M.A., Simmons, A.: Optimal strategies for measuring diffusion
in anisotropic systems by magnetic resonance imaging. J. of Magn. Reson. Med. 42(3),
515-525 (1999)
15. Casas, E., Kunisch, K., Pola, C.: Some applications of BV functions in optimal control and
calculus of variations. ESAIM: Proceedings. Control and Partial Differential Equations 4,
83-96 (1998)
Search WWH ::




Custom Search