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Segmentation of human skull in MRI using statistical shape information from CT data
Author(s) -
Wang Defeng,
Shi Lin,
Chu Winnie C.W.,
Cheng Jack C.Y.,
Heng Pheng Ann
Publication year - 2009
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21864
Subject(s) - computer science , skull , segmentation , cranial vault , artificial intelligence , data set , pattern recognition (psychology) , computer vision , sørensen–dice coefficient , level set (data structures) , image segmentation , anatomy , medicine
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. J. Magn. Reson. Imaging 2009;30:490–498. © 2009 Wiley‐Liss, Inc.