Weakly-supervised segmentation of non-Gaussian images via histogram adaptation

Here we apply an active contour model that allows for arbitrary intensity distributions inside and outside the boundary of an object to be segmented in an image. Computationally, we estimate intensity histograms both inside and outside the current boundary estimate, and use these histograms to define an image energy as their log-likelihood ratio. Training the model with accurate example segmentations is unnecessary; initialization with a crude, user-provided segmentation is sufficient.