## 6 Discussion
In this investigation we computed for each component method 100 components as well as 7 components. Then for each case we compute the maximum CNR for each defects and compare the results. As one can note in Figure 11 for most of the methods regardless the number of component we found that there is no significant variation between the maximum CNR computed for 7 components and the one computed for 100 components. We can see in plots Figure 11b,j a noticeable difference between the two max CNR in terms of amplitude. In both cases the CNR computed for 7 components offers higher amplitude than the one computed for one hundred components. For both cases the shape of the plot are quite similar, nevertheless we can surprisingly observe a slight offset of the maximum CNR. Other less significant difference between the maximum SNR computed for 7 and 100 component are visible for some of the other plot. For most of the other methods despite a slight difference regarding the amplitude the shape of the plot are very similar and does not shows any offset. One can note that the difference between CNR are higher for the data acquired at 145 Hz than at 120 Hz. Looking at Table 3 and Figure 13 one can note that computing a higher number of component increases the execution time, especially regarding for Trust-region ICA and infomax ICA, where the computation time for 100 components 13 times slower than the one for 7 components at frequency 120 Hz. Fast ICA for 7 and 100 components are quite close, 1.17 and 1.09 time slower at frequency of 120 Hz and 145 Hz, respectively. For other ICA methods the difference between computing 7 components and 100 varies from 2.24 to 13.74 times slower at a frequency of 120 Hz, and from 2.03 to 13.16 times slower at 145 Hz. Although Trust-region ICA and quasi-newton ICA offers among the quicker computation time between the ICAs methods, the number of component to compute does not affect their computational time equally. Even method such as PCT which is on PCA that is nowadays a highly optimized algorithm needed two times more time to compute 100 components that 7. Surprisingly we observed that all the methods had a quicker computational time on the data acquired at 120 Hz than 145 Hz. To conclude our investigation regarding the influence of the number of components on the results we computed for each dataset, for both 100 components and 7 components, the mean of the components of all the methods. For a given number of components we computed obtain 6 results, 1 for each method. For simplification we suppose that the result of each method is a third rank tensor, where the first dimension correspond to the number of components, the two other dimensions represents the number of rows and columns respectively of each components. In order to compute the mean component we compute for a given components index the mean of the results for this index. Once the all the mean of the component have computed for all the component index we compute IQR for every matrix coordinates. The result obtained is a matrix of the same number of rows and columns as the component. Figure 12 shows the results we obtained, which leads us to the conclusion that fewer component offer a higher contrast regarding the defective region and therefore makes it easier to identify them. Another investigation we conducted in our study regards the ability of each method to provide a distinctive output for automatic segmentation. To do our experiments we used the algorithm introduces in Figure 6. Table 4 shows the accuracy score computed from the segmented images showed in Figure 15. We also show in Figure 14 the result of the computation of the IQR per pixel from the output of the different methods. The accuracy was compute for each defects separately and then averaged. We can observed that the results regarding the segmentation varies a lot from a method to another. The accuracies scores shows that both Trust-region-ICA as well as Fast-ICA outperform PCT for the data acquired at 120 Hz.