## 4 Discussion
### 3D Thresholding  
The thresholding error was one of the most significant errors compared to the other processes. The thresholding error represents the edge location error considering that the LD and NLD uncertainty of the MSCT system were relatively low. This means that the volume-based segmentation overestimated the bone surfaces of the DICOM images to the STL format with a value of 0.602 mm outside the true surface. Assuming a cranioxillofacial implant case that involves a hole into which an implant is intended to fit, then the thresholding error for the size of the implant would be 2 \(\times\) 0.602 mm = 1.2 mm. Practitioners should consider that this error enables the correction of a systematic effect for the implant. The thresholding error propagates to the implant because the implant was modeled as a negative of the segmented SD head.  
The variations in the thresholding value generated significantly altered SD skull geometry. The results revealed that the data loss of the skull surface was inversely proportional to the area of valid distance. Similarly, the staircase effect or the blocky coarse surfaces of the skull were directly proportional to the standard deviation, and the artefacts were directly proportional to the negative values of the integrated distance.  
When excluding the J70H kernel that included the coarse skull surface comprising of data loss, the staircase effect (blocky data) with sharp edges, and a higher number of artefacts, the selection of an optimal CT reconstruction kernel delimited to the J30S kernel. Since CT Protocol 1 involved lower image thickness of 0.5 mm, it captured a slightly higher amount of the segmented SD head data than CT Protocol 3.  
Though the 300 HU generated a similar and uniformly fine surface data than the 250 HU, it also contained a lower amount of inscribed volume. Comparatively, the 500 HU generated excessive artefacts.  
Even though the higher sharpness enabled by the J70H kernel can be beneficial for bony structures that require a higher spatial resolution, it is also associated with accentuated noise. The optically scanned dry skull was mainly composed of smooth outer bone that was within the line of sight of the optical 3D coordinates measuring system. Thus, the results favored the J30S reconstruction kernel that generated smoother bone surfaces than the J70H kernel. The J70H kernel also exhibited properties that were more prone to the partial volume effect [39] than the J30S kernel. Though the attenuation coefficient and density of distinct matter, i.e., bone (+1000 HU), water (0 HU), and air (-1000 HU), are identifiable, the partial volume effect allocates voxels as soft tissue instead of bone at the bone-to-soft tissue boundary which results in voids and a loss of data.  
The results indicated that the essential step of 3D thresholding remains as one of the critical factors that limit the accuracy of additively manufactured medical constructs. More specifically, the thresholding error was close to the imaging voxel size which has also been reported in the past using four commercially available software packages [71]. Though the use of different software can generally yield accurate geometry, discrepancies can be significant between software packages mainly due to a lack of identical array of control settings and the embedded algorithm for DICOM-to-STL conversion [71]. For example, Osirix MD, which was used in this study, is most probably related to an exact geodesic path algorithm, Dijkstra algorithm, and the fast marching method [71], whereas the Mimics software (Materialise NV, Leuven, Belgium), which is also approved by the United States FDA [72], is embedded with the adapted marching cubes algorithm with the partial volume effect for 3D imaging [73].