BioMed Research International / 2017 / Article / Tab 1

Research Article

3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

Table 1

Quantitative evaluation of the proposed tumor segmentation method. Results are represented as mean ± standard deviation (VOE: volumetric overlap error; RVD: relative volume difference; ASD: average symmetric surface distance; RMSD: root mean square symmetric surface distance; MaxD: maximum symmetric surface distance; DICE: Dice similarity coefficient).

VOE (%)RVD (%)ASD (mm)RMSD (mm)MaxD (mm)DICEDiameter (mm)

Small tumor35.90 ± 7.060.38 ± 25.290.66 ± 0.230.99 ± 0.313.34 ± 1.270.78 ± 0.059.42 ± 2.11
Max47.7637.991.251.746.320.8614.80
Min24.11−31.240.400.692.000.695.54
Large tumor26.62 ± 7.11−3.11 ± 11.050.74 ± 0.351.13 ± 0.544.57 ± 3.400.84 ± 0.0516.95 ± 9.20
Max46.5018.252.403.2720.780.9545.38
Min8.78−18.070.400.681.730.707.61
Average29.04 ± 8.16−2.20 ± 15.880.72 ± 0.331.10 ± 0.494.25 ± 3.030.83 ± 0.0614.99 ± 8.63

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.