## 3 Results
### High-Resolution Skin Ultrasound Images
#### 3.2.2 Luminosity Feature-Based Skin Image Classifications  
A new classification method, based on the luminosity values (0 to 255) from the red, green or blue channels, rather than images themselves, has been developed. To achieve this, we need to analyze the images first, to extract the luminosity feature values.  
Figure 4: The skin layers at different cross-sections in a high-resolution ultrasound image [13].  
Figure 5: The average stratum corneum thickness of different skin sites.  
Expert evaluation of the ultrasound images allows formulating the following observations/hypotheses:  
1. Areas of different colors correspond to different elements of the skin: fat, etc.
2. Properties of skin, e.g., density of the elements, may only depend on the depth. As such, we are not looking for any two-dimensional patterns and the problem is essentially one-dimensional.
3. Coloration of the images is essential, so moving to monochrome representation will lose information. There are three major issues with the ultrasound images, as shown in Figure 3.  
1. The large empty area on top, which is not perfectly black. This is due to the design of the probe.
2. The natural curvature of skin.
3. Possible presence of the gel layer, due to the imperfect contact between the probe's thin rubber film and the skin.  
To rectify the issues, the following three transformations of the images were performed:  
1. Gel was manually removed if present.
2. Curvature was removed algorithmically.
3. Nearly black area on top, if present, was removed algorithmically and only 50% of the image vertical area was kept.  
Figure 6 illustrates the transformations performed.  
Table 4 summarizes the training dataset with total 368 images, modified to remove outliers per site from the original set of images.  
Factor Specification  
The pre-processed images were used as input for the training algorithms. Images were first represented as three matrices of luminosity values from 0 to 255, with each matrix representing red, green or blue channels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Skin Sites** & **Number of Images** \\ \hline
1 & Cheek & 57 \\
2 & Chin & 50 \\
3 & Forarm & 46 \\
4 & Forehead & 60 \\
5 & Lips & 36 \\
6 & Nose & 38 \\
7 & Undereye & 81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The modified ultrasound images at different skin sites.  
Figure 6: The ultrasound skin image and its transformations. (**a**) Original image, (**b**) gel layer removed, (**c**) curvature removed (+50% vertical cull).  
Due to the relatively small number of observations, we chose the following features for classification per channel:  
1. Mean luminosity value.
2. Standard deviation of luminosity value.
3. Median luminosity value.
4. Tail of the luminosity histogram with bin values (150, 170, 200, 256).  
Consequently, there were six features per channel and 18 features on the whole image. Values of those 18 factors were then used as the explanatory variable for the classification. The output variable was a number from 1 to 7, coding the facial site.  
Three standard classifiers were used: Logistic, K-Nearest neighbor and Neural Networks. Training was repeated 10 times and on each iteration the set was split randomly into 20% and 80% groups, representing testing and training sets. Score and accuracy across all runs were then aggregated.  
Table 5 presents a typical average output; the best ever average score we observed was 0.68 or 68%. Score represents the quality of training, while accuracy represents the quality of classification on the testing set. It can be concluded that Logistic classifier performs best. We expect its performance to be improved for larger sample sizes and more rigorous systematic measurements. Random Forest was also originally tried, but it appeared constantly overfit and underclassified.