# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Results
#### 3.4.2 Results on IBSR18 Dataset  
The images in the IBSR18 are already bias field corrected. Hence, we have not applied any bias field correction technique. We conducted the experiments by removing the skull using a ground truth mask. Figure 2 shows the qualitative segmentation results obtained using the proposed method. The main limitation of the IBSR18 dataset is that it considers sulcal CSF as GM. The authors in [54] compared 10 existing methods without considering the sulcal CSF. Following [55; 56], in our study we did not removed the sulcal CSF. We have compared the results of the proposed method with state-of-the-art methods. As all  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**CSF**} & \multicolumn{3}{c}{**GM**} & \multicolumn{3}{c}{**WM**} \\ \cline{2-10}  & **DC** & **HD** & **AVD** & **DC** & **HD** & **AVD** & **DC** & **HD** & **AVD** \\ \hline HMRF-EM [8] & 61.47 \(\pm\) 2.32 & 7.17 \(\pm\) 1.62 & 12.51 \(\pm\) 8.57 & 79.65 \(\pm\) 4.26 & 51.41 \(\pm\) 1.62 & 4.11 \(\pm\) 8.04 & 83.82 \(\pm\) 4.02 & 5.09 \(\pm\) 1.31 & 3.33 \(\pm\) 8.64 \\ FAST-PVE [41] & 54.08 \(\pm\) 3.61 & 7.17 \(\pm\) 1.51 & 12.51 \(\pm\) 7.28 & 78.97 \(\pm\) 2.24 & 5.14 \(\pm\) 0.92 & 4.11 \(\pm\) 6.34 & 85.11 \(\pm\) 2.61 & 50.59 \(\pm\) 1.11 & 3.33 \(\pm\) 6.24 \\ FSL [38] & 79.72 \(\pm\) 3.64 & 4.78 \(\pm\) 1.92 & 9.36 \(\pm\) 5.32 & 87.84 \(\pm\) 2.37 & 5.33 \(\pm\) 0.81 & 5.37 \(\pm\) 6.57 & 88.51 \(\pm\) 2.31 & 5.13 \(\pm\) 1.34 & 8.18 \(\pm\) 4.95 \\ SPM2 [9] & 80.46 \(\pm\) 0.27 & 6.71 \(\pm\) 20.27 & 6.04 \(\pm\) 89.52 & 8.25 \(\pm\) 39.1 & 0.76 \(\pm\) 4.82 & 88.11 \(\pm\) 2.44 & 5.45 \(\pm\) 0.95 & 2.7 \(\pm\) 5.24 \\ FreeSurter [40] & 84.33 \(\pm\) 39.46 & 4.24 \(\pm\) 4.04 & 4.33 \(\pm\) 4.06 & 91.47 \(\pm\) 2.44 & 4.18 \(\pm\) 0.57 & 2.63 \(\pm\) 4.91 & 90.48 \(\pm\) 1.31 & 3.8 \(\pm\) 0.59 & 2.77 \(\pm\) 5.64 \\ MSSEG [42] & 89.95 \(\pm\) 1.62 & 4.18 \(\pm\) 1.62 & 4.18 \(\pm\) 1.62 & 91.24 \(\pm\) 1.62 & 4.31 \(\pm\) 1.62 & 2.87 \(\pm\) 1.62 & 89.98 \(\pm\) 1.62 & 4.39 \(\pm\) 1.62 & 2.91 \(\pm\) 1.62 \\ RSKFM [25] & 90.06 \(\pm\) 2.79 & 40.66 \(\pm\) 1.61 & 4.31 \(\pm\) 4.52 & 92.31 \(\pm\) 2.61 & 4.21 \(\pm\) 1.64 & 2.31 \(\pm\) 3.42 & 90.51 \(\pm\) 1.52 & 4.31 \(\pm\) 0.42 & 2.81 \(\pm\) 5.29 \\ GSFKM [25] & 91.23 \(\pm\) 2.82 & 4.08 \(\pm\) 1.53 & 4.28 \(\pm\) 3.26 & 92.51 \(\pm\) 2.32 & 4.13 \(\pm\) 0.42 & 2.11 \(\pm\) 3.61 & 90.62 \(\pm\) 1.66 & 2.17 \(\pm\) 5.41 \\ MIFCM\_S [20] & 98.21 \(\pm\) 3.02 & 4.23 \(\pm\) 1.42 & 4.51 \(\pm\) 3.14 & 98.91 \(\pm\) 2.41 & 4.41 \(\pm\) 0.31 & 2.97 \(\pm\) 3.59 & 97.28 \(\pm\) 1.71 & 4.59 \(\pm\) 0.82 & 3.01 \(\pm\) 5.16 \\ MIFCM\_Y [20] & 92.65 \(\pm\) 30.36 & 3.96 \(\pm\) 1.69 & 3.91 \(\pm\) 3.02 & 93.61 \(\pm\) 2.51 & 4.23 \(\pm\) 0.32 & 2.16 \(\pm\) 3.42 & 92.61 \(\pm\) 1.68 & 4.31 \(\pm\) 0.84 & 2.17 \(\pm\) 5.24 \\
**Proposed Method (consensus clustering)** & **93.64 \(\pm\) 2.15** & **3.16 \(\pm\) 1.31** & **3.85 \(\pm\)** 2.06 & **94.71 \(\pm\)** 2.30 & **4.01 \(\pm\)** 0.21 & **2.06 \(\pm\)** 2.96 & **93.17 \(\pm\)** 1.32 & **4.26 \(\pm\)** 0.81 & **2.07 \(\pm\)** 4.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results comparison with state-of-the-art methods on OASIS dataset (Mean \(\pm\) std).  
Figure 1: Segmentation results on OASIS dataset: first column, original image; second column, ground truth; and third column, segmentation result fused on ground truth.  
the considered methods have used DC alone as an evaluation metric, Table 2 shows the results only on the DC of the IBSR18 dataset. From this comparison, it is clear that the proposed model has better performance concerning CSF, GM, and WM when compared to the other methods.