We propose a variational segmentation model based on statistical information ofintensities in an image. The model consists of both a local region-based energy and a globalregion-based energy in order to handle misclassification which happens in a typical statisticalvariational model with an assumption that an image is a mixture of two Gaussian distributions.
We find local ambiguous regions where misclassification might happen due to a small differencebetween two Gaussian distributions. Based on statistical information restricted to the localambiguous regions, we design a local region-based energy in order to reduce the misclassification.
We suggest an algorithm to avoid the difficulty of the Euler-Lagrange equations of theproposed variational model.