  In this paper, a novel framework for automated liver segmentation via a level
set formulation is presented. A sparse representation of both global
(region-based) and local (voxel-wise) image information is embedded in a level
set formulation to innovate a new cost function. Two dictionaries are build: A
region-based feature dictionary and a voxel-wise dictionary. These dictionaries
are learned, using the K-SVD method, from a public database of liver
segmentation challenge (MICCAI-SLiver07). The learned dictionaries provide
prior knowledge to the level set formulation. For the quantitative evaluation,
the proposed method is evaluated using the testing data of MICCAI-SLiver07
database. The results are evaluated using different metric scores computed by
the challenge organizers. The experimental results demonstrate the superiority
of the proposed framework by achieving the highest segmentation accuracy
(79.6\%) in comparison to the state-of-the-art methods.
