32 TESTING
level 7 was applied to flatten the surface then we tried several filters. A cutoff filter between 1 and 3mm to separate waviness from roughness. A threshold filter between 10 and 20% of highest altitude to isolate the KP then a size filter between 0.1 and 0.3mm2
to remove the artefactual smallest
objects. We studied the following parameters: Density, Volume, Perimeter, Surface, Maximum thickness.
Mountains Map Software (Digital Surf, France)
The same methodology was applied with Mountains Map to study the same parameters.
Statistical analysis
Excel 365 was used to perform statistical analysis in the efficacy study. A two-tailed paired Student's t-test was used.
Results
Dermatoscopic camera The 2D acquisitions provide high definition pictures and show clearly the KP lesions (Fig 6). Thanks to high resolution, it is possible to zoom in on the picture without losing information for a more detailed visualization of lesions. In the blue cube, it can be observed sharp bumps blocking the hair follicles and lesions are closed by a white scaly plug corresponding to the accumulation of keratin and surrounded by a red halo characteristic of an inflammation (Fig 7). The 3D acquisition allows the visualization of KP volume (Fig. 8 and 9) providing a valuable feature not only for illustrating acquisition but also to have a better idea of KP severity. Moreover, the C- Cube software helps to select a KP lesion and calculate some classical topographic parameters such as Sa, Sq, Sz, Sv, Sp, Sz. Sa and Sq represent the average
distance (respectively arithmetic and quadratic) to the average elevation, often called “roughness”. The valley or pic depth (Sv or Sp) and total amplitude (Sz) is also interesting for KP assessment (exemple of datas reported in the Table 1).
AEVA Software set up
Due to the technology we employed, KP were converted as holes instead of being considered as bumps. It was decided to work with the original fringes projection acquisition which were then filtered appropriately. As the original acquisitions contained too much information, we thus used a cutoff filter to separate waviness from roughness, the former being chosen to allow a better visualization of KP, with regular and sharp edges (Fig 10). Then, we have investigated various filtering possibilities using 3 filters with 3 modalities for each of them (⇔27 possibilities).
PERSONAL CARE NORTH AMERICA Figure 10: Separation of roughness and waviness. Roughness Waviness Silflo replica 3D acquisition by fringes projection
Figure 11: KP selection regarding cutoff size. Cutoff 1mm
Cutoff 2mm
Cutoff 3mm
Threshold 10%
Threshold 15% Figure 12: KP selection regarding threshold size.
Threshold 20%
KP > 0.1mm2
KP > 0.2mm2 Figure 13: KP selection regarding surface size. March 2020
KP > 0.3mm2
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