## 6 Discussion  
The goal of this study was to investigated the performances of different ICA algorithms for processing PT data. We chose two state-of-the-art methods in order to provide a fair comparison. This study focus on three features, the contrast of each defects with its closest neighbourhood, the accuracy of detection for a given segmentation algorithm, and the time of execution. We investigated these feature using two dataset of the same samples acquired with different frequencies, which allow us to extend also observe the influence of the frequency of acquisition on the three features we investigated. For each case we provide quantitative results. From Table 2 we can observe that for most of the methods investigated, the CNR for the defect from the sequence acquired at 145 Hz offers a higher CNR than at 120 Hz. The highest difference are observed for the PPT where for 80% of the defects have a higher CNR at 145 Hz than 120 Hz. Quasi-newton and Trust-region-ICA shows a higher CNR at 145 Hz for 76% of the defects. 72% of the defects have a higher CNR at 145 Hz with the Fast-ICA method. Infomax-ICA, PICARD and PICARD-O have better CNR at 145 Hz for 68% of the defects. PCT shows 60% of the defects have a higher CNR at 145 Hz. For the data acquired at the frequency of 120 Hz we made the following observations. The defects with a lateral size of 3 mm, have a higher CNR for 60% of the ICA method compared with PCT. Five over six investigated ICA methods offer higher CNR than PPT. The same number of methods outperform the PCT regarding the CNR for the defect with the smallest lateral size, located at the deepest depth (L = 3 mm, d = 1 mm). For this particular defect all the ICA methods outperformed the PPT. The best CNR is obtained by the Quasi-Newton approach. We can observe that for smaller and shallowest PCT exceeds both ICA and PPT. The differences between ICAs methods and PCT regarding the scores vary from 7% to 17% lower. PPT reaches the highest difference ratio with PCT with a score of 79% lower. PCT, PPT, and ICA methods have higher CNR values for 60%, 4%, and 36% of the defects. Among the ICAs method the best results were obtained by Fast-ICA and PICARD methods. As one can note ICAs methods except for smaller and deeper defects does not better than the PCT method. However, ICAs CNR results are often close to those of the PCT. For the experiment conducted with the data acquired at the frequency of 145 Hz, we observed that the ICAs method exceed PPT and PCT for 72% of the defects. PCT and PPT beat the ICAs methods for 12% and 16% of the defects, respectively. For deeper and smaller defects, Trust-region ICA beats both PPT and PCT. Nonetheless, the increase in CNR, even if slightly higher, is similar to PCT. One can note that ICA methods are better than state-of-the-art approaches for deeper defects. For the shallowest defects (depth = 0.2 mm), the CNR scores for the ICAs methods are quite close regardless of the method. Similar observations can be made for the defects with a lateral size of 3 mm located at a depth of 0.2 mm and a depth of 0.4 mm, for all the methods. Between ICA methods, Trust ICA, Fast ICA, Quasi-newton ICA, and PICARD in 64%, 16%, 16%, and 4% of defects has the maximum CNR, respectively. Among all \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Accuracy Score**} \\ \cline{2-3}  & \multicolumn{2}{c}{**Frequency (Hz)**} \\ \cline{2-3}  & **120** & **145** \\ \hline PCT & 0.7310 & 0.8333 \\ Trust region ICA & 0.9340 & 0.767 \\ PICARD-O & 0.1270 & 0.6619 \\ PICARD & 0.4397 & 0.7959 \\ Fast ICA & 0.7319 & 0.8915 \\ Infomax ICA & 0.2279 & 0.4832 \\ Quasi newton ICA & 0.3758 & 0.7589 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy score of different ICA methods segmentation. the methods, Trust ICA, Quasi-Newton ICA, Fast ICA, PPT, and PCT obtained 32%, 24%, 16%, 16%, and 12% of the maximum CNR scores, respectively. As mentioned in Section 4.2 we conducted a limited investigation about the selection of the components.