3D segmentation of the liver using free-form deformation based on boosting and deformation gradients

Authors:
Hong Zhang;Lin Yang;David J. Foran;John L. Nosher;Peter J. Yim
Affiliations:
Department of Biomedical Engineering, Rutgers University and The Center for Biomedical Imaging and Informatics, UMDNJ-RWJMS, Piscataway, NJ;The Center for Biomedical Imaging and Informatics and Department of Radiology, UMDNJ-RWJMS, Piscataway, NJ;The Center for Biomedical Imaging and Informatics and Department of Radiology, UMDNJ-RWJMS, Piscataway, NJ;Department of Radiology, UMDNJ-RWJMS, Piscataway, NJ;Department of Radiology, UMDNJ-RWJMS, Piscataway, NJ
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Abstract

This paper presents a novel automatic 3D hybrid segmentation approach based on free-form deformation. The algorithms incorporate boosting and deformation gradients to achieve reliable liver segmentation of Computed Tomography (CT) scans. A free-form deformable model is deformed under the forces originating from boosting and deformation gradients. The basic idea of the scheme is to combine information from intensity and shape prior knowledge to calculate desired displacements to the liver boundary on vertices of deformable surface. Boosting classifies the 3D image into a binary mask and the edgeflow generates a force field from the mask. The deformable surface deforms iteratively according to the force field. Deformation gradients cast restriction at each deformation step. The deformation converges to a stable status to achieve the final segmentation surface.