## 5 Result and Discussion
### Application for MRI-Based Brain Tumor  
The real datasets were from the Dr. Soetomo General Hospital (RSUD), Surabaya. The MRI sequences in this study were from T1 MEMP + C and T2 FLAIR; both were from the axial point of  
Figure 5: Segmentation result of NI02 for different methods. Figure 6: The misclassification ratio (_MCR_) comparison for SI01, SI02, NI01, and NI02 segmentation results. view. T1 MEMP + C was the MRI sequence in which the tumor area was enhanced by contrast media, which is the material given to patients, either orally or injected, before doing MRI scanning. T2 FLAIR was the MRI sequence without contrast media; this sequence visualized the swelling or edema of the brain tumor more visibly; therefore, the ROI of T2 FLAIR was usually larger than T1 MEMP + C.  
We tried to segment about 75 images from the T1 MEMP + C and T2 FLAIR sequences. The tumor area was set as the ROI, while another area was set as a non-ROI. All sequences used in this study passed medical approval, and the GT was also conducted under medical judgment. All MRI images were analyzed under normal and neo-normal models. This paper only displays four sample images. The sample datasets are visualized in Figure 7, while the histograms are shown in Figure 8.  
Figure 7a,c includes the sample from the T1 MEMP + C sequence, while Figure 7b,d is the sample from the T2 FLAIR sequence. The sample data images are named IM01, IM02, IM03, and IM04. The first applied procedure was preprocessing. It was done to eliminate the fat around the skull, which could have interfered with the segmentation process. Figure 8 shows the image histogram after preprocessing. Unlike Figure 8b-d, the multimodality in Figure 8a is not clearly visible since the frequency of the other modes was very small. Due to the multimodalities present in the MRI data pattern, applying the mixture model framework was considered appropriate for this case. The 75 datasets were segmented under the GMM, the GGMM, the FSSN-MM, and the MSNBurr-MM. The \(OSC\) was calculated to provide the optimum number of clusters. Table 1 shows the sample of the \(OSC\) for IM01-04. The optimum value of the \(OSC\) led to the optimum number of clusters. The optimum number of clusters for the Nenomimo for IM01 and IM03 was \(K=3\), while IM02 and IM04 reached the optimum number at \(K=4\). Various results were found with the GMM; for IM01, the optimum number of clusters was \(K=7\), while IM02 and IM04 reached the optimum number at \(K=4\), and IM03 reached the optimum number at \(K=5\). The GGMM had the cluster optimum at \(K=4\) for IM01 and IM04, \(K=5\) for IM02, and \(K=3\) for IM03. The segmentation results for the optimum clusters are visualized in Figure 9.  
Based on segmentation results in Figure 9, we can see that the ROI from the GMM still contained more noise than the GGMM, the FSSN-MM, and the MSNBurr-MM. For empirical comparison, the \(MCR\) from 75 dataset was found. Figure 10 shows the comparison of the \(MCR\) for all dataset using different methods. The \(MCR\) from Figure 10 shows that the minimum value was reached by the MSNBurr-MM, while the FSSN-MM gave similar results. This indicated that the Nenomimo have a better performance than the model-based normal distribution. The average value of the \(MCR\) for the FSSN-MM was about 0.02761, while the MSNBurr-MM had an average \(MCR\) value of about 0.02644. This value showed that the Nenomimo could segment the real dataset with a misclassification ratio of less than 3%.