## 3 Experiments
### Experiments with SVM
Table 2 shows the results obtained by the best configuration. We compute accuracy values obtained with and without the use of Morphological Operators to isolate their contribution within the overall segmentation procedure. Secondly, in order to measure the ability of the SVM to segment under an increased level of heterogeneity, we select the 10 cases where the SVM gave the best results in intra-patient analysis. The Series of 6 cases extracted from the 10 cases, previously selected are considered for training, for a total of 120 learning stages. Table 3 shows accuracy values obtained by the best SVM, with and without the use of Morphological Operators, tested on the overall 25 cases. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **M1** & **M2** \\ \hline \multirow{2}{*}{_DSC_} & Mean & 0.808 & 0.878 \\  & Var & 0.008 & 0.003 \\  & Min & 0.549 & 0.757 \\  & Max & 0.908 & 0.963 \\ \hline \multirow{2}{*}{_P_} & Mean & 0.824 & 0.884 \\  & Var & 0.006 & 0.003 \\  & Min & 0.648 & 0.749 \\  & Max & 0.927 & 0.963 \\ \hline \multirow{2}{*}{_R_} & Mean & 0.796 & 0.873 \\  & Var & 0.012 & 0.003 \\  & Min & 0.476 & 0.764 \\  & Max & 0.923 & 0.963 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dice (_DSC_), Precision (_P_), Recall (_R_) values obtained using M1 and M2 strategy and tested on the overall cases under study. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **SVM** & **SVM + MO** \\ \hline \multirow{2}{*}{_DSC_} & Mean & 0.701 & 0.693 \\  & Var & 0.011 & 0.035 \\  & Min & 0.462 & 0 \\  & Max & 0.844 & 0.897 \\ \hline \multirow{2}{*}{_P_} & Mean & 0.747 & 0.696 \\  & Var & 0.026 & 0.047 \\  & Min & 0.437 & 0 \\  & Max & 0.997 & 0.997 \\ \hline \multirow{2}{*}{_R_} & Mean & 0.737 & 0.769 \\  & Var & 0.035 & 0.057 \\  & Min & 0.41 & 0 \\  & Max & 0.983 & 0.990 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dice (_DSC_), Precision (_P_), Recall (_R_) values obtained by the segmentation procedure trained on one case according to M2 strategy, with and without the use of Morphological Operators (MO) and tested on the overall 25 cases under study. Looking at values in Tables 2 and 3 in more detail, we notice that the application of Morphological Operators is not always advantageous. However, when studying individual cases, we have noticed that under-estimation and over-estimation errors occur systematically when the pathology occupies a very small volume (under the 100 elements), and it is inserted in a highly heterogeneous context. Figure 8 illustrates an example with a slice (Slice 1) including with a metastasis remarkable small. The refinement accomplished by the Morphological Operators deletes all the true positive elements identified by the SVM classifier. On the contrary, the segmentation masks of the larger pathological area in the slice (Slice 2) shown in Figure 9, indicate that the segmentation strategy benefits from the allied use of SVM and Morphological Operators. Table 4 lists the numerical results of the cases illustrated in Figures 8 and 9. Automatic segmentations were evaluated qualitatively through visual inspection. The complete strategy, including the combined use of SVM and Morphological Operators, have been judged satisfactory. The limitations of the segmentation procedure, inherent to specific cases, as illustrated above, are considered acceptable and manageable with interactive phases devoted to manual refinements of the automated results.