## 6 Discussion
We can see that PCT segmentation does not perform well for defects located at 1 mm, for the smallest defects as well as for defects with a lateral size of 5 mm located at a 0.2 and 0.4 mm from the surface. Fast-ICA method performs better than PCT for smaller defects, but miss the smallest defect located a depth depth than 0.2 mm. Interestingly the defects with a lateral size lower than 10 mm located at a depth 0.8 mm are not detected while defects located at the depth of 1 mm with a lateral size of 10 mm as well as 5 mm are partially detected. Trust-Region-ICA shows more consistant results than PCT and Fast-ICA. This method was able to detect even partially all the defects located a depth of 0.2 mm. All but the smallest defect for the depths of 0.4 mm, 0.6 mm, and 0.8 mm from the surface. It was able to made two partial detection for the defect with a lateral size of 10 mm and 7 mm located at the depth of 1 mm from the surface. Quasi-Newton-ICA offers similar results compared with PCT. Nonetheless most of the detection most of the detection made with this methods are partial, unlike PCT. ICA-Infomax, PICARD, PICARD-O, does shows many detection mostly partial. The number of detection as well as the quality of detection does make these method uninteresting for defect detection. In short for the data acquired at 120 Hz, the segmentation approach shows that Fast-ICA algorithm outperform all the other method regarding the segmentation of defects locate a the depth of 1 mm from the surface while Trust-region-ICA is the method that has detected the highest number of defects. For the data acquired at 145 Hz the results of the segmentation shows that the PCT offers similar results as previously observed. Fast-ICA method underperform for defect located at depth lower than 0.4 mm from the surface, compared with the results obtained with the data acquired at 120 Hz. Trust-region-ICA offers better performances for the defect located at a depth of 1 mm from the surface but fails for this depth to detect the smallest defect. It also fails to detect the largest defect located at the depth of 0.4 mm from the surface, while being able to detect all the other defects. For the rest of the defect the performance of Trust-region-ICA are mostly similar to what has been present for the data acquired at 120 Hz. Quasi-Newton-ICA result improved noticeably. It is able to detect all the defects of the shallowest depth, and for the other depth all the defects with a lateral size from 7 mm and higher. It is also able to detect the defect with a lateral size of 5 mm located at a depth of 0.8 mm. It shows also some partial detection in non defective region. ICA-Infomax, PICARD, PICARD-O, shows better results on the data acquired at 145 Hz than they did on the previous data. In spite of these improvement these methods shows very poor results compared with the other methods. To sum up, on the data acquired at a frequence of 120 Hz both Fast-ICA and Trust-region-ICA performed well compared with PCT. On the data acquired at a frequence of 145 Hz, Fast-ICA's underperformed while Quasi-Newton-ICA performances were improved. Trust-region-ICA results were quite similar. To conclude the different aspect of our study, we have observed that for most of the method we investigated computing a smaller number of components does not significantly affect the CNR results. Have fewer component can makes the identification of the defects in the sample easier. The number of component to compute can significantly affect the computation depending the method. Using a simple segmentation approach in association with the accuracy score we also showed that the frequence of acquisition can significantly affect the results of some algorithms. Among those we investigated Fast-ICA, Trust-region-ICA and Quasi-Newton ica provided consistent output, while have a reasonable computational time.