Abstract
An approach is described which has the potential to unify edge preserving smoothing with segmentation based on differential edge detection at multiple scales. The analysis of n-D data is decomposed into independent 1-D problems. Smoothing in various directions along 1-D profiles through n-D data is driven by local structure separation, rather than by local contrast. Analytic expressions are obtained for the derivatives of the edge preserved 1-D profiles. Using these expressions, multidimensional edge detection operators such as the Laplacian or second directional derivative can be composed and used to segment n-D data. The smoothing and segmentation algorithms are applied to simulated 4-D medical images.
Acknowledgments
The authors thank the University of North Carolina Medical Imaging Research Laboratory for making the MCAT phantom available.
This work was supported by the National Heart, Lung, and Blood Institute of the US Department of Health and Human Services under grant P01-HL25840; by the Director, Office of Science, Office of Biological and Environmental Research, Medical Sciences Division of the US Department of Energy under contract DEAC03- 76SF00098; and by the University of California MICRO program. This work was developed in part using the resources at the US Department of Energy National Energy Research Scientific Computing (NERSC) Center.
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References
Kitamura, K., Iida, H., Shidahara, M., Miura, S., Kanno, I.: Noise reduction in PET attenuation correction using non-linear Gaussian filters. IEEE Trans. Nucl. Sci., 47 (2000) 994–999
Reutter, B.W., Algazi, V.R., Huesman, R.H.: Computationally efficient nonlinear edge preserving smoothing of n-D medical images via scale-space fingerprint analysis. In Ulma, M. (ed.), 2000 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (2001, in press)
Reutter, B.W., Gullberg, G.T., Huesman, R.H.: Direct least-squares estimation of spatiotemporal distributions from dynamic SPECT projections using a spatial segmentation and temporal B-splines. IEEE Trans. Med. Imag., 19 (2000) 434–450
Reutter, B.W., Klein, G.J., Huesman, R.H.: Automated 3-D segmentation of respiratory-gated PET transmission images. IEEE Trans. Nucl. Sci., 44 (1997) 2473–2476
Segars, W.P., Lalush, D.S., Tsui, B.M.W.: Modeling respiratory mechanics in the MCAT and spline-based MCAT phantoms. In Seibert, J.A. (ed.), 1999 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (2000) 985–989
Sternberg, S.R.: Grayscale morphology. Comput. Vis. Graph. Image Proc., 35 (1986) 333–355
Wang, Y.-P., Lee, S.L.: Scale-space derived from B-splines. IEEE Trans. Patt. Anal. Mach. Intell., 20 (1998) 1040–1055
Weickert, J.: A review of nonlinear diffusion filtering. In Ter Haar Romeny, B., Florack, L., Koenderink, J., and Viergever, M. (eds.), Scale-Space Theory in Computer Vision: Proceedings of the First International Conference (1997) 3–28
Weickert, J., Ter Haar Romeny, B.M., Viergever, M.A.: Efficient and reliable scheme for nonlinear diffusion filtering. IEEE Trans. Image Proc., 7 (1998) 398–410
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Huesman, R.H., Reutter, B.W., Algazi, V.R. (2001). Nonlinear Edge Preserving Smoothing and Segmentation of 4-D Medical Images via Scale-Space Fingerprint Analysis. In: Insana, M.F., Leahy, R.M. (eds) Information Processing in Medical Imaging. IPMI 2001. Lecture Notes in Computer Science, vol 2082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45729-1_45
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DOI: https://doi.org/10.1007/3-540-45729-1_45
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