# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Autism Spectrum Disorder Detection Using Proposed Method  
Additionally, the proposed consensus clustering method has been evaluated on a practical autism spectrum disorder (ASD) detection problem. We used publicly available Autism Brain Imaging Data Exchange (ABIDE) data for this study. The ABIDE dataset contains 1112 subjects, 571 of them normal, and 531 of them with Autism Spectrum Disorders. We used 1054 of the 1112 subjects for this study, and the rest were rejected for improper segmentation using voxel-based morphometry (VBM). In this study, we employ a feature extraction method based on the VBM [57]. VBM is a fast and automatic method for determining the difference in gray matter structure between normal and and ASD patient brains [58]. In our VBM analysis, unified segmentation, smoothing, and statistical analysis were performed as preprocessing steps. In the unified segmentation step, tissue segmentation, bias correction, and image registration were combined in a single generative \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**GM**} & \multicolumn{2}{c}{**WM**} & \multicolumn{2}{c}{**CSF**} \\ \cline{2-7}  & **mean** & **std** & **mean** & **std** & **mean** & **std** \\ \hline R-FCM [43] & 65.00 & 0.05 & 75.00 & 0.05 & NA & NA \\ NL-FCM [43] & 72.00 & 0.05 & 74.00 & 0.05 & NA & NA \\ FCM [43] & 74.00 & 0.05 & 72.00 & 0.05 & NA & NA \\ HMR-FEM [8] & 74.60 & 0.04 & 89.60 & 0.02 & 12.60 & 0.05 \\ SFCM [44] & 70.60 & 0.06 & 86.60 & 0.03 & 16.60 & 0.07 \\ FANYAS [45] & 71.60 & 0.06 & 88.60 & 0.03 & 11.60 & 0.06 \\ PVC [31] & 70.60 & 0.08 & 83.60 & 0.07 & 13.60 & 0.06 \\ SPMS [46] & 68.60 & 0.07 & 86.60 & 0.02 & 10.60 & 0.05 \\ GAMCMUWE [47] & 78.60 & 0.08 & 87.60 & 0.02 & 15.60 & 0.09 \\ ANN [48] & 70.60 & 0.07 & 87.60 & 0.03 & 11.60 & 0.06 \\ KNN [49] & 79.60 & 0.03 & 86.60 & 0.03 & 16.60 & 0.07 \\ BrainSuit09 [50] & 72.00 & 0.09 & 83.00 & 0.08 & NA & NA \\ SVPASG [51] & 81.60 & 0.03 & 88.60 & 0.02 & 16.60 & 0.07 \\ SPMS [39] & 81.60 & 0.02 & 88.60 & 0.01 & 17.60 & 0.08 \\ ECG-SOM [52] & 73.00 & 0.05 & 76.00 & 0.04 & NA & NA \\ HFS-SOM [52] & 60.00 & 0.09 & 60.00 & 0.08 & NA & NA \\ FAST-PVE-[64] & 78.00 & 0.08 & 86.00 & 0.04 & NA & NA \\ FAST-PVE-[6-KM] & 78.00 & 0.08 & 86.00 & 0.04 & NA & NA \\ RF-CRF [53] & 96.10 & 0.01 & 92.00 & 0.02 & 92.00 & 0.03 \\ RF-CRF [53] & 94.00 & 0.01 & 89.00 & 0.02 & 88.00 & 0.03 \\ PSL [38] & 78.13 & 0.04 & 85.94 & 0.13 & 75.02 & 0.04 \\ Freewriter [40] & 79.62 & 0.06 & 86.17 & 0.12 & 76.42 & 0.06 \\ SPM12 [39] & 82.30 & 0.04 & 89.82 & 0.02 & 78.62 & 0.14 \\ RSKFM [28] & 96.68 & 0.09 & 93.55 & 0.10 & 93.41 & 0.08 \\ GSRCM [29] & 96.72 & 0.03 & 93.58 & 0.02 & 93.43 & 0.02 \\ MIFCM,S [20] & 96.74 & 0.41 & 93.62 & 0.43 & 93.86 & 0.71 \\ MIFCM,Y [20] & 96.82 & 0.15 & 93.64 & 0.15 & 94.02 & 0.15 \\
**Proposed Method consensus clustering** & **97.31** & **0.01** & **94.50** & **0.04** & **95.68** & **0.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result comparison with state-of-the-art methods on IBSR18 dataset only in terms of DC. Figure 2: Segmentation results on IBSR18 dataset: first column, original image; second column, ground truth; and third column, segmentation result fused on ground truth. model [46]. The segmented and registered gray matter images were then smoothed by convolving with an isotropic Gaussian kernel. Here, a 10 mm full-width at half-maximum kernel was employed. A two-sample t-test was performed on the smoothed images, and gray matter volume was used as the covariate. This VBM analysis revealed significant gray matter volume increases in the normal persons in comparison with the ASD patients. The voxel location of significant regions were used as a mask. All segmented gray matter images were used to extract gray matter tissue probability values using a mask. A total of 989 features were obtained. and these were used as an input to the proposed method. Table 3 presents the performance comparison for Autism Spectrum Disorder Detection.