## 1 Introduction
### Brief Theory of Computed Tomography: from Medical to Industrial  
X-ray CT refers to the use of X-rays to digitally dissect a specimen and reveal its interior details [13]. A 2D CT image or a CT slice typically corresponds to a certain small thickness slice of the object being scanned, while a 3D CT image is composed of stacked slices of an entire volume. Therefore, a 2D CT image comprises _pixels_ (picture elements) while a 3D CT image comprises _voxels_ (volume elements). A 2D CT image is obtained by projecting X-rays around the slice plane from a number of orientations and measuring their attenuation. The 2D distribution of X-ray attenuation in the slice plane is then reconstructed using a specialized algorithm. The grey levels in a 2D CT image correspond to the attenuation of X-rays attributed to each _pixel_ in the slice. The 3D distribution describing an entire volume can be created by acquiring a stacked series of 2D slices. Since its widespread adoption for medical imaging, X-ray CT has been adapted to various industrial applications too [14]; for example measuring dimensional and shape quality sometimes in the same time with checking material quality of parts. These parts are often made through machining, casting, thermo-forming, or other methods where measurement of internal dimensions is not possible [15]. The industrial tasks involve imaging denser materials (metals) for a wide range of sizes and resolutions In medical CT systems, low dose X-ray sources with relatively low-energy (<140 keV), large (mm-scale) beam widths, and high-efficiency detectors are used for safety reasons [6]. In industrial CT systems highly penetrating X-ray energies are required (min. 250 keV) for scanning high density materials (e.g., ferrous alloys), such as used in PM for manufacturing automotive gear parts [16]. Enclosures are designed and built for ensuring the safe operation of the X-ray system [17], tailored to the scale of the manufacturing operation and related component characteristics (size, material, and geometrical complexity). The application of purpose built enclosures allows the following characteristics for industrial CT imaging [13]: (1) high penetration capability for dense materials with higher-energy X-rays; (2) enhanced resolution through the use of narrower X-ray beams and more densely packed X-ray detectors; (3) increased signal-to-noise ratio (SNR) through longer exposure times, without compromising health and safety in a manufacturing environment. A major innovation in this research project is the development of algorithms for automated defect detection and recognition to identify the defect features of each composite component and categorise the defects according to their types. Using these algorithms, there is a little need for an inspector to interact with the system that will automatically characterise and classify the defects in the composite components to give information required by the end-user. A prerequisite of this automated software is the establishment of a reference database for defect recognition by similarity analysis. The major difference between the presented ADR system and other CT based state-of-the-art industrial defect recognition techniques, is the use of similarity analysis. Most of the PM manufacturing techniques such as Press-Sinter, Laser Sintering, or Metal Injection Moulding are considered as net or near-net shape production as the finished product does not require further processing. The discussed ADR system is designed to compare the 3D CT images of PM parts with previously created and analysed reference images, so called Golden images. This comparison is only possible because these types of high precision manufacturing techniques are capable of producing such high precision output where the shape and dimensional difference between each part is negligible. Other industrial applications are based on signal analysis like wavelet or principal curvature analysis [18; 19].