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Medical Imaging

Segmentation with Level Sets.

Segmentation (i.e. delineation of anatomical structures in image data) plays a crucial role in medical imaging. Due to the complexity and variability of anatomic shapes, segmentation is still a big challenge.

Contour propagation by initial conditions (40, 100, 150 and 200 iterations)

A wide variety of segmentation techniques have been proposed: a) traditional low-level image processing techniques which consider only local information and generate infeasible object boundaries and b) more robust techniques with deformable models which exploit constraints from the image data (bottom-up) together with “a priori” knowledge about the shape (topdown).

The Institute for Medical and Analytical Technologies is focusing in the field of Medical Imaging on Data-Visualization, -Analysis and Modeling. The study of segmentation techniques is therefore a fundamental issue.

For efficient and robust segmentation of various anatomic structures we implemented the Chan-Vese- algorithm (a deformable model with Level Sets).

Algorithm

The Chan-Vese algorithm is a deformable model approach with contour evolution based on the Mumford-Shah functional, and the level sets of Osher and Sethian.

Results Conclusion

First applications with specific anatomical structures (brain, vessels, spine) led to robust and accurate segmentation results. And a strength of the algorithm is its ability to handle the topology changes (level sets).

Due to the inability to distinguish between different shapes (no model-information) the algorithm is mainly useful for semi-automated segmentation.

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Contact

Prof.

Institute for Medical Engineering and Medical Informatics

FHNW University of Applied Sciences and Arts Northwestern Switzerland School of Life Sciences, Institute for Medical Engineering and Medical Informatics Hofackerstrasse 30 CH - 4132 Muttenz
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