## 3 Results
### High-Resolution Skin Ultrasound Images
#### 3.2.3 Texture-Based Skin Image Classification  
Another image classification method based on image texture was also investigated in this study. In this approach, the original EPISCAN images in DICOM (.dcm) format were cropped to patches of dimension 512 \(\times\) 1024 pixels (Figure 7) and converted, losslessly, to 8-bit gray-scale bitmaps (.bmp). No further pre-processing operation, such as filtering, de-noising, intensity clipping and/or resampling, was applied. Ten types of textural features (five traditional and five based on convolutional networks--see below) were extracted from each image, this way obtaining as many \(\mathbf{M}_{i}\) data matrices, \(i\in\{1,\,\ldots,\,10\}\), each of dimension \(N\times F_{i}\), where \(F_{i}\) indicates the number of features generated by the \(i\)-th descriptor. We applied principal component analysis (PCA) on each of the \(\mathbf{M}_{i}\) and retained the first two principal components to generate scatter plots for exploratory data analysis (Figures 8 and 9). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Min** & \
\begin{tabular}{c} **Score** \\ **Mean** \\ \end{tabular} & **Max** & **Min** &
\begin{tabular}{c} **Accuracy** \\ **Mean** \\ \end{tabular} & **Max** \\ \hline Logistic & 0.57 & 0.61 & 0.64 & 0.43 & 0.50 & 0.59 \\ K-Nearest & 0.62 & 0.65 & 0.68 & 0.30 & 0.39 & 0.42 \\ Neighbour & 0.16 & 0.34 & 0.45 & 0.16 & 0.30 & 0.30 \\ Neural & & & & & & \\ Networks & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The Machine Learning classification results. Figure 8: Scatter plot of the first two principal components from texture features computed through Gabor filters. Marker color shows details about the body part where the scan was taken; marker size and shape show, respectively, the age and gender of the subject. AU = arbitrary units. Figure 7: Sample scans acquired with the EPISCAN system. From left to right: cheek sample from a 28-year-old man; forearm sample from a 59-year-old woman; and lips sample from a 41-year-old woman. The colormap shows relative units (each image has been normalized between 0 and 1). The vertical direction corresponds to the skin depth with the outermost layer at the top of each image. **Traditional ('Hand-Designed') Descriptors**  
Discrete Cosine Filters (DCF)  
Mean and standard deviation of the transformed images processed through a bank of 25 two-dimensional separable filters. The filters were generated via pair-wise outer product of five one-dimensional DCF kernels defined on a sequence of 11 points [29] (25 \(\times\) 2 = 50 features). Mean and standard deviation of the magnitude of the transformed images processed through a bank of 25 Gabor filters with five frequencies and five orientations [29] (525 \(\times\) 2 = 50 features). Laws' masks  
Mean and standard deviation of the transformed images processed through a bank of 25 two-dimensional separable filters. These were generated via pair-wise outer product on the five one-dimensional Laws' kernels [30] (25 \(\times\) 2 = 50 features). Local binary patterns (LBP)  
Concatenation of directional (non-rotation-invariant) local binary patterns histograms [31] computed on eight-pixel digital circles of radius 1 px, 2 px and 3 px (256 \(\times\) 3 = 768 features). Mean and standard deviation of the transformed images processed through a bank of filters based on even and odd Zernike polynomials of order \(r\in\{0,\,\ldots\,,\,6\}\)[29, 32] (42 features). Texture Descriptors Based on Deep Learning  
We considered five learned descriptors from the following pre-trained convolutional networks: DenseNetl21, MobileNet, ResNet50, VGG16 and Xception. Following the approach described in previous works [33, 34, 35], we used the \(L_{1}\)-normalised output of one intermediate layer as image features. Specifically, we retrieved the features from the  
Figure 9: Scatter plot of the first two principal components from texture features computed through Gabor filters. Marker color and shape indicate the probe type. AU = arbitrary units.