# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 3 Results
### Evaluation of Features Extracted and Lesion Classification: Results  
Various experiments were conducted to deduce the classifier with a major goal to assess the best subset of features and compare the performance of training the SVM with single set of features against training with concatenation of features. The Figure 11 illustrates the plot of features for combined dataset versus the \(p\)-values. A good score has a \(p\)-values less than or equal to 0.05 (\(p\leq\) 0.05) whereas \(p>0.05\) is considered a bad score. It is seen from Figure 11, that the shape and pigment network features have good scores. However, the performance scores for color asymmetry index (\(x_{2},x_{3},x_{4}\)), indicates comparably lower scores. Insignificant \(p\)-values for color similarity index for colors red, light brown and dark brown can be observed. This justifies the fact that presence of red is due to vascularization of blood vessels, irrespective of the lesion class and shades of brown are common to both the lesion types (benign and malignant). Regarding texture features, T2 (contrast) performed comparably poor then T1 and T3. Similarly, the role of statistical color features for ROI and NROI can be interpreted from Figure 11. The Table 2 provides the mean and standard deviation values of the features with significant \(p\)-values for combined datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**F** & **Mean** & **SD** & **F** & **Mean** & **SD** \\ \hline AI & 0.69 & 0.94 & VRI & 1509.015 & 1368.13 \\ \hline CI & 2.63 & 3.21 & VGI & 1834.916 & 1303.59 \\ \hline FD & 26.31 & 9.30 & PC1 & 2910.31 & 1911.63 \\ \hline T1 & 39.80 & 17.70 & PC2 & 116.10 & 100.96 \\ \hline T3 & 13.42 & 12.77 & PC3 & 11.62 & 7.95 \\ \hline Cx1 & 13.57 & 12.89 & ER & 6.54 & 0.65 \\ \hline W & 0.10 & 0.31 & EB & 6.62 & 0.44 \\ \hline K & 0.24 & 0.42 & ERI & 6.19 & 0.75 \\ \hline BG & 0.94 & 0.21 & EBI & 6.80 & 0.47 \\ \hline CRG & 0.01 & 0.14 & F1 & 7361.83 & 22,929.7 \\ \hline CGB & 0.94 & 0.05 & F2 & 0.08 & 0.17 \\ \hline CBR & 0.95 & 0.09 & F3 & 0.52 & 0.39 \\ \hline CRK & 0.85 & 0.10 & F4 & 0.06 & 0.43 \\ \hline CGK & 0.99 & 0.05 & F5 & 0.14 & 0.16 \\ \hline CBK & 0.94 & 0.06 & & & \\ \hline CRGI & 0.93 & 0.05 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean and standard deviation values of the features with significant \(p\)-values. Figure 10: Overlap segmentation error for Modified Chan-Vese and Chan-Vese Algorithm. Based on the \(p\)-values the feature set was reduced from 48 to 34 features. The role of single and combined features in lesion diagnosis is given in Table 3. It can be inferred from \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**F** & **Mean** & **SD** & **F** & **Mean** & **SD** \\ \hline CBRI & 0.86 & 0.10 & & \\ \hline VR & 1032.02 & 832.46 & & \\ \hline VG & 1032.52 & 702.47 & & \\ \hline VK & 974.10 & 652.87 & & \\ \hline \hline \end{tabular} Note: F—Feature, SD—Standard deviation, AI—Asymmetry Index, CI—Compactness Index, FD—Fractal dimensions, TI—Coarseness, T3-Directionality, CI—Color symmetry index, W—white, K—Black, BG—Blue Gray, CRG—Color Variation between Red and Green, CGB—Color Variation between Green and Blue, CBR—Color Variation between Blue and Red, CRG—Color Variation between Red and Grey, CGK—Color Variation between Green and Grey, CBK—Color Variation between Blue and Grey, CRG—Color Variation between Red and Green for entire image, CBRI—Color Variation between Blue and Red for entire image VR—Color Variation for Red, VG—Color Variation for Green, VK—Color Variation for grey, VRI—Color Variation for Red for entire image, VGI—Color Variation for Green for entire image, PC1, PC2, PC3—Three Principal components, ER-Entropy for red, EB—Entropy for Blue, ERI—Entropy for Red for entire image, EBI—Entropy for Blue for entire image, Fi–F5—Pigment network features.  
\end{table}