# Design of a Computed Tomography Automation Architecture
## 4 Post-Imaging Techniques
### Post-Processing Automation  
Automation incorporated into the analysis of CT data was proposed for optimizing and planning in lumber production by [PERSON] et al. [30]. The analysis of CT images of wooden logs was conducted using machine vision algorithms for identifying and localizing internal defects. Automatic grading of the lumber products using National Hardwood Lumber Association rules was conducted by using the 3D reconstruction with simulated sawing operations. Sawing strategies were then created from this optimization for the highest value yield recovery of the lumber products. There were two separate contributions made. First, a framework for detection, localization, and 3D reconstruction of internal log defects was proposed. Second, models and algorithms were proposed for lumber production optimization that uses the data gathered from the detected defects. The main goal was to automatically produce a decision aid for planning on how to optimally saw the logs.  
The food industry also has incorporated automated analysis of CT data. For example, the Danish Meat Research Institute (DMRI) uses CT to measure meat-fat distribution in pig carcasses, and this spatial distribution can deliver an optimal path for a robot to automatically cut the pork for a slaughterhouse. \"PigClassWeb\" is the system that handles the large amounts of CT scans acquired by DMRI and enables virtual cuts in a reference pig. There is also an accurate estimate of the weights of cuts before they happen on each carcass.  
YXLON International's MU56 TB [31] is a robotic industrial X-ray system developed for NDT of turbine blades. This system allows for automatic X-ray inspection without operator interaction. Beyond this, YXLON created \"TrueInspect ADR,\" which is an automatic defect recognition software that can be incorporated with the automatic robotic X-ray inspection. The robot handles, moves, and orients the part throughout the CT scan, while the source and detector remain fixed.  
[PERSON] and [PERSON] [32] demonstrated a method for automating flaw detection based on tracking initially identified potential defects. A sequence of X-ray images was taken at several different orientations and compared with an error-free reference image. Flaws were first detected where there lies a significant difference in pixel regions. The key part of this study was to track these potential flaws through the differently oriented images to rule out false positives if they are not detected from most views. The tracking of flaws was accomplished by identifying the projection of the 3D coordinates of the sample onto the 2D coordinates of the X-ray image's pixels, and the coordinate transformation between the differently oriented images to match one image's pixels to their representative pixels in a separate image. This matching of pixels between separate images is called correspondence, which is solved using epipolar geometry. The geometric transformations and correspondence formulas are described in greater detail in Chapter 3 of [PERSON]'s book [33]. [PERSON] and [PERSON] have also more recently worked on incorporating computer vision techniques into X-ray testing for the goal of automating the detection of defects [34]. That contribution included the creation of a dataset of 47,520 small X-ray images (defects and no-defects), the evaluation of 24 computer vision techniques (deep learning, sparse representations, local descriptors, texture features, etc.), and the MATLAB code used to perform the experiments.