Brownian strings: segmenting images with stochastically deformable contours

Robert Grzeszczuk; David N. Levin M.D.

doi:10.1117/12.185227

9 September 1994 Brownian strings: segmenting images with stochastically deformable contours

Robert Grzeszczuk, David N. Levin M.D.

Proceedings Volume 2359, Visualization in Biomedical Computing 1994; (1994) https://doi.org/10.1117/12.185227
Event: Visualization in Biomedical Computing 1994, 1994, Rochester, MN, United States

Abstract

This paper describes an image segmentation technique in which an arbitrarily shaped contour was deformed stochastically until it fitted around an object of interest. The evolution of the contour was controlled by a simulated annealing process which caused the contour to settle into the global minimum of an image-derived 'energy' function. The non-parametric energy function was derived from the statistical properties of previously-segmented images, thereby incorporating prior experience. Since the method was based on a state space search for the contour with the best global properties, it was stable in the presence of image errors which confound segmentation techniques based on local criteria such as connectivity. However, unlike 'snakes' and other active contour approaches, the new method could handle arbitrarily irregular contours in which each inter-pixel crack represented an independent degree of freedom. The method was illustrated by using it to find the brain surface in magnetic resonance head images, to identify the epicardial surface in magnetic resonance cardiac images, and to track blood vessels in angiograms.

Citation Download Citation

Robert Grzeszczuk and David N. Levin M.D. "Brownian strings: segmenting images with stochastically deformable contours", Proc. SPIE 2359, Visualization in Biomedical Computing 1994, (9 September 1994); https://doi.org/10.1117/12.185227

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