# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 5 Results and Discussion
### Evaluating Results for the Proposed SMP Algorithms  
This section evaluates results of the proposed SMP Algorithms in Section 3.1.2.3. Figure 18 show the original (left) and enhanced (right) images of notch 1 under the 2 mm-thick rubber layer, obtained at three standoff distances of 0.1 mm, 0.25 mm, and 4 mm. As is evident from Figure 18, the original images (left) were blurry, and no information could be gained from the images of notches under the 2 mm-thick rubber layer with a standoff distance of 4 mm, as shown in Figure 18c. However, the right-hand-side images show the results of image enhancement over the original images. It is obvious that the enhanced images can show up the existence of notches. Figure 19 shows the original and enhanced image of notches 1 and 2 under the 4 mm-thick rubber layer obtained at a standoff distance of 0.1 mm. As is evident from Figure 19a, the original image was blurred, and no information could be gained from an image with a standoff distance of 0.1 mm, while the notches were clearly observable on the enhanced images (Figure 19b). By applying the image-enhancement algorithm, the notches in the specimens became clear, as shown in Figure 18 (a(right), b(right), c(right)), and Figure 19b, thus meeting one of the goals of this investigation. In order to make progress with the algorithms, the enhanced image of notch 1, Figure 18a, was used to apply three image processing algorithms, as presented in the Section 3.1.2. To save space, the corresponding images obtained for Figure 18b,c after the SCM process, are provided in Appendix A. Figure 20 shows the images obtained for notch 1 in each step of Algorithm 1. Figure 19: The (**a**) original and (**b**) enhanced images of notches 1 and 2 under the 4 mm-thick rubber layer, obtained at a standoff distance of 0.1 mm. Figure 18: Original (left) and enhanced (right) images of notch 1 under the 2 mm-thick rubber layer, obtained at three standoff distances: (**a**) 0.1 mm, (**b**) 0.25 mm, and (**c**) 4 mm. Figure 21 shows the images obtained for notch 1 in each step of Algorithm 2. Figure 22 shows the images obtained for notch 1 in each step of Algorithm 3. In order to facilitate the discussion, in Algorithm 3, the developed Fuzzy c-mean thresholding algorithm was applied, to segment the image. However, the obtained threshold value was \(2.05\times 10^{-1}\), while for [PERSON]'s thresholding method and the traditional fuzzy-c-mean thresholding algorithm, it was \(1.7\times 10^{-1}\) and \(2.4\times 10^{-1}\), respectively. Figure 23 shows the comparison of the segmented images of notch 1, obtained with Algorithms 1-3. According to the segmented images, the developed Fuzzy c-mean thresholding algorithm (Algorithm 3) outperformed the other two algorithms. As is evident from Figure 23c, the true edges, which were detected by the developed Fuzzy c-mean thresholding algorithm, were more accurate with less noise, while Figure 23a,b include redundant edges and noises, which represent the segmentation error. A further investigation of the developed algorithm was carried out, by comparing its performance with the traditional Fuzzy c-mean thresholding algorithm and [PERSON]'s thresholding algorithm. Figure 24  
Figure 21: Images of notch 1 under the 2 mm-thick rubber layer: (**a**) original and after (**b**) average filtering, (**c**) the Canny edge detection algorithm obtained at a standoff distance of 0.1 mm. Figure 22: Images of notch 1 under the 2 mm-thick rubber layer obtained at a standoff distance of 0.1 mm: (**a**) original and after (**b**) average filtering, and (**c**) thresholding by the developed fuzzy c-mean thresholding algorithm.