Nonparametric shape priors for active contour-based image segmentation

Kim, Junmo and Çetin, Müjdat and Willsky, Alan S. (2007) Nonparametric shape priors for active contour-based image segmentation. Signal processing, 87 (12). pp. 3021-3044. ISSN 0165-1684

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When segmenting images of low quality or with missing data, statistical prior in- formation about the shapes of the objects to be segmented can significantly aid the segmentation process. However, defining probability densities in the space of shapes is an open and challenging problem. In this paper, we propose a nonpara- metric shape prior model for image segmentation problems. In particular, given example training shapes, we estimate the underlying shape distribution by extend- ing a Parzen density estimator to the space of shapes. Such density estimates are expressed in terms of distances between shapes, and we consider the L2 distance between signed distance functions for shape density estimation, in addition to a distance measure based on the template metric. In particular, we consider the case in which the space of shapes is interpreted as a manifold embedded in a Hilbert space. We then incorporate the learned shape prior distribution into a maximum a posteriori estimation framework for segmentation. This results in an optimization problem, which we solve using active contours. We demonstrate the effectiveness of the resulting algorithm in segmenting images that involve low-quality data and occlusions. The proposed framework is especially powerful in handling "multimodal" shape densities.
Item Type: Article
Uncontrolled Keywords: image segmentation; curve evolution; level set method; nonparametric density estimation; shape prior
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 14 Feb 2007 02:00
Last Modified: 04 Sep 2019 14:28

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