Image Processing Reference
In-Depth Information
Typical line profiles extracted from the phantom image before filtering (thin line) and
after filtering (bold line) are shown in Figure 6.7 , showing the filter's ability to preserve
edges while removing noise.
In cardiac images, anisotropic filtering still preserves contrast at the myo-
cardium interfaces: it clearly appears in 1 NEX image aquisition as shown in
Figure 6.8 . The relevant line profiles before (thin line) and after anisotropic
filtering (bold line) are shown.
Such results are important for the later segmentation operation by an auto-
matic procedure.
REFERENCES
1.
Clarke, L.P., Velthuizen, R.P., Camacho, M.A., Heine, J.J., Vaidyanathan, M., Hall,
L.O., Thatcher, R.W., and Silbiger, M.L. (1995). MRI segmentation: methods and
applications. Magn. Reson. Imaging. 13: 343-368.
2.
Wang Y. and Lei T. (1994). Statistical analysis of MR imaging and its applications
in image modelling. Proceedings of the IEEE International Conference on Image
Processing and Neural Networks I: 866-870.
3.
Ahn, C.B., Song, Y.C., and Park, D.J. (1999). Adaptive template filtering for signal-
to-noise ratio enhancement in magnetic resonance imaging. IEEE Trans. Med.
Imaging 18(6): 549-556.
4.
Rank, K. and Unbehauen, R. (1992). An adaptive recursive 2-D filter for removal
of Gaussian noise in images . IEEE Trans. Image Process. 1: 431-436.
5.
Westin, C.F., Wigström, L., Loock, T., Sjökvist, L., Kikinis, R., and Knutsson, H.
(2001). Three-dimensional adaptive filtering in magnetic resonance angiography.
Magn. Reson. Imaging . 14: 63-71.
6.
Xu, Y., Weaver, J.B., Healy, D.M., and Lu, J. (1994). Wavelet transform domain
filters: A spatially selective noise filtration technique. IEEE Trans. Image Process .
3, 747-757.
7.
Mallat S. and Hwang, W. (1992). Singularity detection and processing with wave-
lets. IEEE Trans. Inform. Theory 38: 617-643.
8.
Crouse, M.S., Novak, R.D., and Baraniuk, R.G. (1998). Wavelet-based signal
processing using hidden Markov models. IEEE Trans. On Signal Proc. 46:
886-902.
9.
Coifman, R. and Donoho, D. (1995). Wavelets and Statistics, Lecture Notes in
Statistics . Berlin: Springer-Verlag.
10.
Zaroubi, S. and Goelman, G. (2000). Complex denoising of MR data via wavelet
analysis: application for functional MRI. Magn. Reson. Imaging 18: 59-68.
11.
Placidi, G., Alecci, M., and Sotgiu, A. (2003). Post-processing noise removal
algorithm for magnetic resonance imaging based on edge detection and wavelet
analysis. Phys. Med. Biol. 48: 1987-1995.
12.
Perona, P. and Malik, J. (1990). Scale space and edge detection using anisotropic
diffusion. IEEE Trans. Pattern Anal. Machine Intell . 12(7): 629-639.
13.
Gerig, G., Kubler, O., Kikins, R., and Jolesz, F.A. (1992). Nonlinear anisotropic
filtering of MRI data. IEEE Trans. Med. Imaging 11(2): 221-232.
14.
Alvarez, L., Lions, P.L., and Morel, J.M. (1992). Image selective smoothing and
edge detection by nonlinear diffusion. SIAM J. Number. Anal. 29(3): 845-866.
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