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Proceedings Paper

Area prior constrained level set evolution for medical image segmentation
Author(s): Ismail Ben Ayed; Shuo Li; Ali Islam; Greg Garvin; Rethy Chhem
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Paper Abstract

The level set framework has proven well suited to medical image segmentation1-6 thanks to its ability of balancing the contribution of image data and prior knowledge in a principled, flexible and transparent way. It consists of evolving a curve toward the target object boundaries. The curve evolution equation is sought following the optimization of a cost functional containing two types of terms: data terms, which measure the fidelity of segmentation to image intensities, and prior terms, which traduce learned prior knowledge. Without priors many algorithms are likely to fail due to high noise, low contrast and data incompleteness. Different priors have been investigated such as shape1 and appearance priors.7 In this study, we propose a simple type of priors: the area prior. This prior embeds knowledge of an approximate object area and has two positive effects. First, It speeds up significantly the evolution when the curve is far from the target object boundaries. Second, it slows down the evolution when the curve is close to the target. Consequently, it reinforces curve stability at the desired boundaries when dealing with low contrast intensity edges. The algorithm is validated with several experiments using Magnetic Resonance (MR) images and Computed Tomography (CT) images. A comparison with another level set method illustrates the positive effects of the area prior.

Paper Details

Date Published: 10 March 2008
PDF: 6 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691402 (10 March 2008); doi: 10.1117/12.770579
Show Author Affiliations
Ismail Ben Ayed, GE Healthcare (Canada)
Shuo Li, GE Healthcare (Canada)
Ali Islam, St. Joseph's Health Care (Canada)
Greg Garvin, St. Joseph's Health Care (Canada)
Rethy Chhem, London Health Sciences Ctr. (Canada)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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