Liver Segmentation by an Active Contour Model with Embedded Gaussian Mixture Model based Classifiers

Automatic liver segmentation is a crucial step for diagnosis and surgery planning. To extract the liver, its tumors and vessels, we developed an active contour model with an embedded classifier, based on a Gaussian mixture model fitted to the intensity distribution of the medical image. The difference between the maximum membership of the intensities belonging to the classes of the object and those of the background is included as an extra speed propagation term in the active contour model. An additional speed controlling term slows down the evolution of the active contour when it approaches an edge, making it quickly convergent to the ideal object. The developed model has been applied to liver segmentation. Some comparisons are made between the Geodesic Active Contour, C-V (active contour without edges) and our model. As the experiments show, our model is accurate, flexible and suited to extract objects surrounded by a complicated background.