# A Measurement Software for Professional Training in Early Detection of Melanoma
## 2 Materials and Methods
### Color-Based Detection of Dermoscopic Criteria  
The automatic measurement and classification of the dermoscopic structures most dependent on the chromatic distribution of the image pixels (i.e., blue-whitish veil, regression, and irregular pigmentation) is performed through the adoption of suitable logistic model trees (LMTs) [21]. In detail, the machine learning technique is adopted to classify the lesion map, i.e., the set of the chromatically homogeneous regions (low-level structures) resulting from segmentation performed through either principal component analysis (PCA) [15] or statistical region merging (SRM) [22]. The vector \(x\) of the feature descriptors for each region includes the red, green, and blue (RGB), hue, saturation, and intensity (HSI), and CIELUV components of the corresponding pixels; in addition, the percentage ratio of the regional pixels with respect to the total number of the lesion map pixels is considered. The result of the LMT training is depicted in Figure 2a regarding the automatic detection of the blue-whitish veil criterion: According to different ranges for the hue mean value of the region to be analyzed, three patterns (blue, red, or polychromatic) are highlighted by the corresponding logistic regression models. \begin{table}
\begin{tabular}{c c c c c} \hline
**ELM** & **Irregular** & **Regression Structures** & **Irregular Pigmentation** & **Irregular Dots/Globules** \\ \hline
**Score** & & 1 & & \\ \hline \multirow{3}{*}{**Definition**} & Radially and & White and gray-blue areas, & Brown, gray, and black areas of diffuse & Black, brown, or blue round structures \\  & asymmetrically arranged & peppering, multiple & pigmentation with & irregularly \\  & linear or extensions at the & blue-gray dots associated & irregular shape or & distributed within \\  & edge of the lesion & & disruption and abrupt end & the lesion \\ \hline \multirow{3}{*}{**Histological correlates**} & Confluent radial junctional & Areas of loss of & Hyperglmentation & Aggregates of \\  & nests of melanocytes & pignellations, with scattered & throughout all levels of & pigment of stratum \\  & & melanophages & epidermis or upper dermis & corneum, junctional, or dermis location \\ \hline
**Main features** & Structures & Color & Color & Structures \\ \hline \multirow{3}{*}{**Feature extraction**} & Structural analysis & SRM & SRM & SRM \\  & SRM & & structural analysis \\ \hline
**Feature classification** & Statistical analysis & Logistic model tree & Logistic model tree & Statistical analysis \\ \hline \end{tabular}
\end{table} 
Table 2: The automatic system based on Seven-Point Checklist: Definition and detection methods of minor dermoscopic criteria. Figure 2: Detection of the blue-whitish veil: (**a**) Logistic model tree with regression models Fi(x) (with i = 1, blue-veil region, and i = 2, no blue-veil region); (**b**) lesion map (feature extraction); (**c**) regions classified as blue-whitish veil. On the basis of the mean and standard deviation for the hue (\(\sigma_{H}\)), saturation (\(\mu_{S},\ \sigma_{S}\)), and intensity (\(\mu_{I},\ \sigma_{I}\)) components, the functions \(F_{i}(x)\) are adopted to compute the probability \(P_{i}\) that each segment of the lesion map (see Figure 2b) belongs to an area characterized by the blue-whitish veil according to:  
\[P_{i}=\frac{e^{F_{i}(x)}}{\sum_{j=1}^{2}e^{F_{j}(x)}} \tag{1}\]  
where the classes \(i=1\) and \(i=2\) correspond respectively to presence and absence of the dermoscopic criterion. An example of detection result is depicted in Figure 2c. The segments of the lesion map not associated with the blue-whitish veil are then classified by further LMT models as area of either regression (see Figure 3) or irregular pigmentation.