# Algorithm Development for the Non-Destructive Testing of Structural Damage
## 5 Results and Discussion
### Evaluating Results for the Proposed SCM Algorithms  
This section evaluates results of the proposed SCM Algorithms in Section 3.1.2.2. The original images of the sample for composite material are shown in Figure 12, and they demonstrate that the raw images do not provide much observable information, while four defects exist and must be seen. To enhance the indications of the desired targets, three proposed algorithms were run using the same condition. The first algorithm was based on one-, two-, and three-fold multiplication of the wavelet transform, with the Fourier transform of the image, followed by the multi-threshold segmentation method and morphological operation \"erosion\", to remove the small elements from the image to achieve the target. Figure 13 demonstrates the total process of this segmentation. The first row of images in Figure 13 shows the process as performed by a \"single\" multiplication of the wavelet and Fourier transform, while the second and third rows show the process as performed by two- and three-fold multiplication of the wavelet and Fourier transform, respectively. However, the obtained result through the single multiplication (row 1), Figure 13d, shows the best result in this section, as it determines three out of four defects, while the other results only detect two defects. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Result** & **Description** \\ \hline Centroid & [38.7045 27.9205] & Mass center of the region \\ BoundingBox & [35.5000 2.5000 7 64] & Smallest rectangle containing the region \\ FilledImage & [64 \(\times\) 7 logical] & Image with the size of bounding box for the region \\ FilledArea & 176 & Number of ’on’ pixels \\ MajorAxisLength & 68.7051 & Number of pixels for the major axis of the ellipse \\ MinorAxisLength & 5.8664 & Number of pixels for the minor axis of the ellipse \\ Orientation & 88.0654 (degree) & Angle between the MajorAxisLength and the x-axis \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Properties of the image region, results, and descriptions. Figure 12: Microwave image of the sample for the composite material: (**a**) magnitude and (**b**) phase. Figure 14 shows the results obtained by the second algorithm. This figure also demonstrates the total process for one-, two- and three-fold multiplication of Gaussian, with the Fourier transform of the image, followed by the multi-threshold segmentation method, and morphological operation \"erosion\", to remove the small elements from the image to achieve the target. The first row of images in Figure 14 shows the process performed by a \"single\" multiplication of the Gaussian and Fourier transform, while the second and third rows of images show the process performed by two- and three-fold multiplications of Gaussian and Fourier transforms, respectively. However, the results obtained through two-fold multiplication (Figure 14d, row 2) shows the best-segmented result, in this section, as it determines four out of four defects, with less noise than the other results in Figure 14.  
Figure 15 shows the results that are obtained through the Algorithm 3. This figure also shows the total process of target segmentation using fuzzy c-mean total process of target segmentation using fuzzy c-mean clustering, followed by multi-thresholding and erosion, (**a**) original image, (**b**) fuzzy c-mean clustering, (**c**) erosion by structuring element object 10, and (**d**) erosion by structuring element object 15. The comparison of segmentation results using the above three algorithms shows that the results achieved by \"Algorithm 2\", Figure 14, outperform the other two algorithms. In Figure 14, the best demonstration is for the \"two-fold multiplication of Gaussian with the Fourier transform\" which is followed by a multi-threshold segmentation method and morphological operation \"erosion\" (Figure 14, row 2b). The image shows a clear view of four separated defects with less noise and redundant objects.