Nonparametric methods for image segmentation using information theory and curve evolution

In this paper, we present a novel 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 fast level set methods to implement the resulting evolution. The evolution equations are based on nonparametric statistics, and have an intui...

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Alan S. Willsky, Anthony J. Yezzi Jr., John W. Fisher III, Junmo Kim, Müjdat Çetin
• Keywords: ICIP 2002, Image Pixel Intensities, Image Processing, Image Segmentation Problems, Nonparametric Density Estimates