Non-parametric Mixture Model Based Evolution of Level Sets

We present a novel region based level set algorithm. We first model the image histogram with non-parametric mixture of probability density functions(PDFs). The individual densities are estimated using a recently proposed PDF estimation method which relies on a continuous representation of the discrete signals. Prior probabilities are calculated using an inequality constrained least squares method. The log ratio of the posterior probabilities is used to drive the level set evolution. We also take into account the image artifact called the partial volume effect, which is quite important in medical image analysis. Results are presented on natural as well as medical two dimensional images. Visual inspection of our results show the effectiveness of the proposed algorithm