A Statistical Shape Model for the Liver
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Efficient Semiautomatic Segmentation of 3D Objects in Medical Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An improved level set for liver segmentation and perfusion analysis in MRIs
IEEE Transactions on Information Technology in Biomedicine
Improved fully automatic liver segmentation using histogram tail threshold algorithms
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
IEEE Transactions on Image Processing
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Modern epidemiological studies analyze a high amount of magnetic resonance imaging (MRI) data, which requires fully automatic segmentation methods to assist in organ volumetry. We propose a fully automatic two-step 3D level set algorithm for liver segmentation in MRI data that delineates liver tissue on liver probability maps and uses a distance transform based segmentation refinement method to improve segmentation results. MR intensity distributions in test subjects are extracted in a training phase to obtain prior information on liver, kidney and background tissue types. Probability maps are generated by using linear discriminant analysis and Bayesian methods. The algorithm is able to differentiate between normal liver tissue and fatty liver tissue and generates probability maps for both tissues to improve the segmentation results. The algorithm is embedded in a volumetry framework and yields sufficiently good results for use in epidemiological studies.