PURPOSE: To automatically segment the skull from the MRI data using a model-based three-dimensional segmentation scheme. MATERIALS AND METHODS: This study exploited the statistical anatomy extracted from the CT data of a group of subjects by means of constructing an active shape model of the skull surfaces. To construct a reliable shape model, a novel approach was proposed to optimize the automatic landmarking on the coupled surfaces (i.e., the skull vault) by minimizing the description length that incorporated local thickness information. This model was then used to locate the skull shape in MRI of a different group of patients. RESULTS: Compared with performing landmarking separately on the coupled surfaces, the proposed landmarking method constructed models that had better generalization ability and specificity. The segmentation accuracies were measured by the Dice coefficient and the set difference, and compared with the method based on mathematical morphology operations. CONCLUSION: The proposed approach using the active shape model based on the statistical skull anatomy presented in the head CT data contributes to more reliable segmentation of the skull from MRI data.
PURPOSE: To automatically segment the skull from the MRI data using a model-based three-dimensional segmentation scheme. MATERIALS AND METHODS: This study exploited the statistical anatomy extracted from the CT data of a group of subjects by means of constructing an active shape model of the skull surfaces. To construct a reliable shape model, a novel approach was proposed to optimize the automatic landmarking on the coupled surfaces (i.e., the skull vault) by minimizing the description length that incorporated local thickness information. This model was then used to locate the skull shape in MRI of a different group of patients. RESULTS: Compared with performing landmarking separately on the coupled surfaces, the proposed landmarking method constructed models that had better generalization ability and specificity. The segmentation accuracies were measured by the Dice coefficient and the set difference, and compared with the method based on mathematical morphology operations. CONCLUSION: The proposed approach using the active shape model based on the statistical skull anatomy presented in the head CT data contributes to more reliable segmentation of the skull from MRI data.