Area prior constrained level set evolution for medical image segmentation

Ismail Ben Ayed; Shuo Li; Ali Islam; Greg Garvin; Rethy Chhem

doi:10.1117/12.770579

10 March 2008 Area prior constrained level set evolution for medical image segmentation

Ismail Ben Ayed, Shuo Li, Ali Islam, Greg Garvin, Rethy Chhem

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 691402 (2008) https://doi.org/10.1117/12.770579
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

The level set framework has proven well suited to medical image segmentation^1-6 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.

Citation Download Citation

Ismail Ben Ayed, Shuo Li, Ali Islam, Greg Garvin, and Rethy Chhem "Area prior constrained level set evolution for medical image segmentation", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 691402 (10 March 2008); https://doi.org/10.1117/12.770579

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