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Automatic Liver Segmentation using Multiple Prior Knowledge Models and Free-Form Deformation

In this paper, an automatic and robust coarse-to-fine liver image segmentation method is proposed. Multiple prior knowledge models are built to implement liver localization and segmentation: voxel-based AdaBoost classifier is trained to localize liver position robustly, shape and appearance models are constructed to fit liver shape and appearance models to original CT images. Free-form deformation is incorporated into segmentation process to improve the model’s ability of refining liver boundary. The method was tested on IBSI 2014 VISCERAL challenge datasets and the result demonstrates that the proposed method is robust and efficient.