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.
CITATION STYLE
Reutter, B. W., Algazi, V. R., & Huesman, R. H. (2001). Nonlinear edge preserving smoothing and segmentation of 4-D medical images via scale-space fingerprint analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2082, pp. 431–437). Springer Verlag. https://doi.org/10.1007/3-540-45729-1_45
Mendeley helps you to discover research relevant for your work.