Exploiting User Labels with Generalized Distance Transforms Random Field Level Sets
Where Published: International Symposium on Biomedical Imaging
We present an approach for exploiting user labels with random field level sets in image segmentation. A sparse set of user labels is propagated to the rest of the image by computing a generalized distance transform which takes into account image intensity information. The region-based level set formulation is modified to use random field level sets whose range is restricted to the probability values. These two ideas are combined in a single level set functional. Improved results are shown on a liver segmentation task.
