Image Segmentation Based on the Statistical Variational Formulation Using the Local Region Information

Abstract

ABSTRACT. We propose a variational segmentation model based on statistical information of intensities in an image. The model consists of both a local region-based energy and a global region-based energy in order to handle misclassification which happens in a typical statistical variational 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 difference between two Gaussian distributions. Based on statistical information restricted to the local ambiguous 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 the proposed variational model.

CoReHA 2.0: A Software Package forIn VivoMREIT Experiments

“…In order to get the imaging domain, we have employed statistically reinstating method (SRM) [22] on MR image, which is a level-set-based segmentation method. After the boundary of the object including electrodes is segmented by SRM, one can operate additional manual work to configure the object without electrodes or to modify the local geometry.…”