Details

Autor(en) / Beteiligte

• Junmo Kim
• Fisher, J.W.
• Yezzi, A.
• Cetin, M.
• Willsky, A.S.

Titel

A nonparametric statistical method for image segmentation using information theory and curve evolution

Ist Teil von

• IEEE transactions on image processing, 2005-10, Vol.14 (10), p.1486-1502

Ort / Verlag

New York, NY: IEEE

Erscheinungsjahr

2005

Quelle

IEEE Xplore

Beschreibungen/Notizen

• In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Furthermore, our method, which does not require any training, performs as good as methods based on training.