Fig 1.
Flowchart of the proposed algorithm.
Fig 2.
Histogram of thirty abdominal CT images.
Fig 3.
Relationship between simplex mesh and virtual triangle mesh.
Fig 4.
Force determination on the circumscribed sphere of the defined triangular mesh.
(a) shows the construction of internal force for one triangle facet on the deformable model; (b) shows the cross-section between plane and the sphere Si.
Fig 5.
Adaptive triangular facet decomposition.
Table 1.
Pseudo code for local adaptive triangular facet decomposition.
Fig 6.
Nine intermediate deformation results of the proposed method.
(T) shows the number of iterations.
Fig 7.
Segmentation results of the proposed method and the conventional DSM-based method.
(A), (B) and (C) are three different data sets; (a) and (b) show the initialization and the finalized states of the deformation model; (c) shows the 3D meshes of the segmented liver.
Fig 8.
Comparison of the segmentation accuracies for different methods.
(A) the conventional DSM; (B) DSM constraint by gradient image; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition.
Fig 9.
Final segmentation results of the proposed method over different image sections.
(S) shows the number of the image section.
Fig 10.
Segmentation results of the proposed method for three different CT data sets.
(A), (B) and (C) demonstrate results of three different data sets.
Fig 11.
Segmentation results of four livers (A to D).
(A1) to (D1) show the meshes of the liver; (A2) to (D2) show the wired grid of the liver; (A3) to (D3) show the ground truth of the liver surface; (A4) to (D4) show the color map of the segmentation error on the surface of the liver.
Table 2.
Segmentation results of the proposed method over 10 groups of data sets.
Table 3.
Comparison of the segmentation accuracies of the proposed method and the other seven up-to-date methods.