Nonlinear Edge Preserving Smoothing and Segmentation of 4-D Medical Images via Scale-Space Fingerprint Analysis

Authors:
Bryan W. Reutter;V. Ralph Algazi;Ronald H. Huesman
Affiliations:
-;-;-
Venue:
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Year:
2001

Citing 4
Cited 0

Grayscale morphology

Computer Vision, Graphics, and Image Processing
Scale-Space Derived From B-Splines

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review of Nonlinear Diffusion Filtering

SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Efficient and reliable schemes for nonlinear diffusion filtering

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.