Nonparametric Shape Priors for Active Contour-based Image Segmentation
Junmo Kim, Müjdat Çetin, and Alan S. Willsky
EURASIP European Signal Processing Conference (EUSIPCO) 2005
When segmenting images of low quality or with missing
data, statistical prior information 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 nonparametric shape prior model for image
segmentation problems. In particular, given example training
shapes, we estimate the underlying shape distribution by
extending a Parzen density estimator to the space of shapes.
Such density estimates are expressed in terms of distances
between shapes, and we propose two distance metrics that
could be used in this framework. 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, involving multiple
classes of objects.