## 4 Experimental Results  
In this section, real CT images are processed with the procedure shown above. The configuration parameters of the CT scanner used can be found in Table 1. A 3D CT image was acquired for a ferrous PM ring sample. A photograph of the ring is shown in Figure 5a with its 3D CT image shown in Figure 5b. To illustrate the effectiveness of the image processing procedure, two 2D slices were taken from the 3D CT image with a distinct defect identified by a red circle in each, as shown in Figure 6. Note that the 'golden image' restriction relaxes here since the defects in the two 2D slices are at different locations and they can be the substitutional 'golden image' for each other. Details regarding this change in operation are provided below. Both 2D images are de-noised first using a Wiener filter and the resultant images are shown in Figure 7, which shows that the filtered images are smoother than the images in Figure 6, i.e., with reduced noise.  
\begin{table}
\begin{tabular}{c c} \hline Type of Equipment: X-Tek HMXCT 225 & Source to Detector Distance: 1112 mm \\ Detector: Perkin Elmer 1620 16-bit flat-panel & Source to object distance: 125 mm \\ Focal spot/source size: 0.005 mm & Magnification: 8.9 \\ Target Material: Tungsten (W) & Tube voltage: 210 kV \\ Beam angle: 90\({}^{\circ}\) & Tube current: 280 \(\mu\)A \\ Exposure: 354 ms & Filters: 2 mm Copper at source \\ Number of frames averaged: 4/projection & IQI: NA \\ Number of projections: 1000 & Voxel Resolution: 0.023 \(\mu\)m\({}^{3}\) \\ Rotation Angle Step: 0.36 degrees & Reconstruction Algorithm: Filter back projection \\ \hline \end{tabular}
\end{table}
Table 1: Configuration parameters of the Computed Tomography (CT) scanner.  
Figure 5: Photo and 3D CT image of a powder metallurgy valve ring sample (ring diameter: Ø24 mm). (**a**) Photo; (**b**) 3D CT image.  
After image filtering, one crucial step is the image registration based on the intensity distribution and the subsequent image subtraction to highlight the defect [30; 31]. For comparison, the two overlapped images before and after image registration are shown in Figure 8a,b, respectively. The red and green outlines in Figure 8a represent the contours of the two different parts from Figure 7 when they are placed on top of each other. This difference is caused by the misalignment of the two images and is being removed through the image registration. Note that the red colour represents the part from Figure 7, left, while the green represents the part from Figure 7, right. In Figure 8b, it can be seen that the two images are almost totally overlapped or matched. This means that a good accuracy has been achieved for image registration. On this basis, image subtraction can be carried out. As mentioned previously, one of the two images can be used as the 'golden' image for the other to calculate the respective change in grey level brought by the defect in the latter. To remove the influence of the defect in each 'golden' image, a truncation is made, i.e., when the value of a grey level after image subtraction is negative, it is set to zero. The two difference images after image subtraction are shown in Figure 9a,b, respectively.  
In Figure 9, defects are highlighted in each image, but with multiple artefacts present, i.e., a new background. The entropic thresholding described above is used to segment the defect in each image from the noise background. The binary images after segmentation are shown in Figure 10. Some small residual spots are still visible in each image of Figure 10. Note that the purple circle represents the part edge and green-coloured objects represent defects. To remove these artefacts, the opening morphological operation is implemented with a disk structure of size 80 pixels. The smoothed images  
Figure 6: Original 2D CT slices of the PM valve ring sample with defects.  
Figure 7: After de-noising with Wiener filter.