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

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.