## 4 Results and Discussion  
The experiment is implemented using Visual Studio 2010 C# with Intel(r) Core(tm) i5 CPU @ 2.80 GHz and 8 GB RAM with 90 ultrasonography images of wrists containing a ganglion cyst.  
Since the articulated point of the proposed method lies in the new fuzzy rule-based stretching, we compare the effect of the proposed stretching with the previous approach used in [24], as shown in Figure 9, to demonstrate its better contrast.  
In the quantization process, we used FCM, as demonstrated in Figure 6. However, there is another alternative for intelligent quantization--ART2 learning. ART2 is also an unsupervised real-time stable learning algorithm that does not suffer from the local minima, and it was very successful in addressing the automatic soft tissue extraction problem [24]. We compare the proposed FCM with the ART2 applied in [24], as shown in Figure 10.  
Figure 8: Ganglion cyst extraction with labelling. (**a**) Candidate cyst, (**b**) cyst after labelling.  
Figure 9: Comparison of fuzzy Stretching. (**a**) Previous [24]. (**b**) proposed.  
As can be seen from Figure 10, in this specific problem domain, where the purpose of pixel clustering is to stretch the intensity, our proposed FCM quantization (lines of Figure 10b) appears to be more effective than ART2 quantization when used in [24] (lines of Figure 10a) with the same input images. One possible explanation for this is that, in ART2 quantization, the vigilance parameter is defined in a static manner before execution, as shown in Table 2, whereas the proposed FCM takes the cluster based on the fuzzy membership rate, meaning that there are more chances of misclassification in ART2. Moreover, the proposed image-enhancement algorithm makes a more robust decision on pixel classification during the FCM process, whereas fuzzy ART suffers from low-contrast, irregular-intensity distribution of given input images, as shown in Figure 10. However, ART2 performed better than FCM in other diagnosis problems [30]; therefore, this performance comparison is limited to this specific problem domain.  
The accuracy of the automatic extraction of a ganglion cyst in this experiment is summarized in Table 3. The correctness of each cyst extraction is based on the pathologists' agreement over the same input image.  
We show some examples of clearer, more successful extractions of the proposed FCM, as compared with the ART2 method, in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**ART2**} & \multicolumn{2}{c}{**FCM**} \\ \hline \multirow{2}{*}{\# of Images} &
\begin{tabular}{c} Vigilance \\ Parameter \\ \end{tabular} & \multirow{2}{*}{\# of Clusters} & \multirow{2}{*}{Weight} & \# of Initial \\  & & & & Clusters \\ \hline
90 & 0.1 & 16 & 2 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment Parameters.  
Figure 10: Comparison of quantization: ART2 in [24] vs. proposed FCM. (**a**) ART2, (**b**) FCM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **ART2**[23] & **FCM** \\ \hline Correct & 80 & 86 \\ Incorrect & 10 & 4 \\ Accuracy (\%) & 88.9 & 95.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of Cyst Extraction.