# Positron Emission Tomography Image Segmentation Based on [PERSON]'s Intuitionistic Fuzzy Sets
## 6 Discussion  
In this paper, a new A-IFSs-based segmentation algorithm is proposed to increase the accuracy of tumor delineation in PET images. This is a crucial step in order to minimize the damage by future treatments, but optimally including the entire extent of the diseased tissue. In the proposed methodology, we introduced an A-IFSs-based segmentation approach that, unlike the existing methodologies, works without any previous processing or human  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Number of Images: 40** & \\ \hline  & **Min** & **Average** & **Max** \\ \hline
**t40\%** & 0.16199 & 0.52214 & 0.96322 \\
**t50\%** & 0.28319 & 0.68626 & 0.98023 \\
**t58\%** & **0.31577** & 0.70475 & 0.98688 \\
**K-Means** & 0.04167 & 0.65334 & 0.98688 \\
**FCM** & 0.02005 & 0.46292 & 0.98586 \\
**AP** & 0.28319 & 0.69397 & **1** \\
**A-IFSs** & 0.09229 & **0.76492** & **1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: IoU algorithm comparison results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Min** & **Average** & **Max** \\ \hline
**t40\%** & 0.97498 & 0.99223 & 0.99988 \\
**t50\%** & 0.98842 & 0.99693 & 0.99995 \\
**t58\%** & **0.99693** & 0.99777 & 0.99997 \\
**K-Means** & 0.99092 & 0.99732 & 0.99997 \\
**FCM** & 0.86800 & 0.97388 & 0.99996 \\
**AP** & 0.98847 & 0.99696 & **1** \\
**A-IFSs** & 0.99235 & **0.99843** & **1** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pixel accuracy algorithm comparison results.  
Figure 7: Algorithm comparison with pixel accuracy.  
interaction defining a ROI, any seed position, or SBR value. Moreover, being an AIFSs-based methodology, and considering that AIFSs were proven to efficiently deal with image uncertainties in the past, the proposed methodology was able to better deal with the PET images' uncertain tumor boundaries. [PERSON]'s intuitionistic fuzzy index values are used for representing the hesitance of an expert on determining whether a pixel of the image belongs to a non-healthy tissue or a healthy tissue. In order to verify its effectiveness, five existing representative methods were used for comparison [25; 45; 50; 51; 52]. Considering solely the fixed thresholds methods, the fixed threshold \(t58\%\), having been experimentally selected for our data set, obtained the second-best IoU and accuracy values. Regarding the other tested methods, FCM had the highest distribution of values and the lowest IoU values while the K-means and AP methods showed similar performance, with AP achieving better IoU scores despite the higher distribution of the accuracy results. Overall, the experimental results show that, despite not using previous information or making use of human interaction, the proposed method has achieved the best performance among all methods tested showing more precision and repeatability. Notably, the overlapping of the target area and the predicted area increases significantly with this method, representing an improvement, outperforming all of the other methodologies tested for tumor delineation in PET images. The hierarchical partition of the pixel intensity classes of the method together with a non-binary definition of class membership seems to be the main reason for the superior performance over the other methods. However, since the method relies on image intensity clustering, this may be understood as a limitation, and future work should include spatial feature information.