## 2 Materials and Methods
### Evaluation of Attributes on Test Set  
The classifiers that have been trained with the attribute subsets were applied to an external test set of 199 images (ETS1) and compared to the results of manual segmentation of the same test set. All classification results were binarized into two classes: worm and background. Quantitation was performed using the Analyze Particles function. For graphical evaluation of the false positive (FP) area, the manual classification image was subtracted from the machine learning classification result. For the quantitation of the true positive (TP) area, the manual classification result was inverted and subtracted from the machine learning classification. This process is illustrated in Figure 2. The condition positive (P) areas were considered as the quantitation results from the manual process and the background of those images is seen as the condition negative (N) area. True negative (TN) and false negative (FN) true positive rate (TPR), true negative (TNR), accuracy (ACC), MCC, precision (PPV), and F1 value (F1) were calculated as follows:  
\[\text{TN}=\text{N}-\text{FP},\text{FN}=\text{P}-\text{TP}\]  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}},\text{ TN}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ranking** & **Attributes** & **Ranking** & **Attributes** \\ \hline
0.9717 & Entropy\_16\_256 & 0.692 & Membrane\_projections\_0\_19\_1 \\ \hline
0.9717 & Entropy\_16\_64 & 0.692 & Membrane\_projections\_5\_19\_1 \\ \hline
0.9717 & Entropy\_16\_128 & 0.692 & Membrane\_projections\_3\_19\_1 \\ \hline
0.8915 & Entropy\_32\_256 & 0.6905 & Entropy\_32\_64 \\ \hline
0.8915 & Entropy\_32\_128 & 0.6862 & Gabor\_2\_1\_0\_0\_5\_0\_2.0 \\ \hline
0.8845 & Variance\_16\_0 & 0.6832 & Gabor\_1\_4\_0\_1\_0\_2\_2.0 \\ \hline
0.8287 & Hessian\_Eigenvalue\_2\_32\_0 & 0.6828 &
\begin{tabular}{c} Hessian\_Normalized\_Eigenvalue\_ \\ Difference\_16.0 \\ \end{tabular} \\ \hline
0.8265 & Variance\_32\_0 & 0.6827 & Median\_16.0 \\ \hline
0.8186 & Laplacian\_16\_0 & 0.6656 & Gabor\_1\_1\_0\_1\_0\_0\_2.0 \\ \hline
0.7814 & Laplacian\_32\_0 & 0.6654 & Hessian\_32.0 \\ \hline
0.7714 & Gabor\_1\_10\_0\_25\_0\_2\_0 & 0.6642 & Membrane\_projections\_1\_19\_1 \\ \hline
0.759 & Entropy\_16\_32 & 0.6469 & Gabor\_2\_1\_0\_0\_25\_0\_2.0 \\ \hline
0.7534 & Gabor\_1\_2\_0\_1\_0\_0\_2.0 & 0.6446 & Sobel\_filter\_16.0 \\ \hline
0.7509 & Gabor\_1\_4\_0\_2\_0\_0\_2.0 & 0.6127 & Hessian\_Trace\_16.0 \\ \hline
0.7473 & Mean\_16.0 & 0.6091 & Entropy\_32\_32 \\ \hline
0.7434 & Hessian\_Trace\_32.0 & 0.6077 & Gabor\_2\_4\_0\_2\_0\_2\_2.0 \\ \hline
0.741 & Gabor\_1\_1\_0\_0\_5\_0\_2.0 & 0.6074 & Gabor\_2\_4\_0\_1\_0\_2\_2.0 \\ \hline
0.7384 & Hessian\_16.0 & 0.599 & [PERSON]\_Eigenvalue\_2\_16.0 \\ \hline
0.7277 & [PERSON]\_1\_4\_0\_1\_0\_0\_2.0 & 0.5965 & Membrane\_projections\_4\_19\_1 \\ \hline
0.7213 & Maximum\_16.0 & 0.5858 & [PERSON]\_Determinant\_32.0 \\ \hline
0.7106 & Membrane\_projections\_2\_19\_1 & 0.5853 & [PERSON]\_2\_1\_0\_1\_0\_0\_2.0 \\ \hline
0.7088 & [PERSON]\_1\_4\_0\_2\_0\_2\_2.0 & 0.5836 & Structure\_smallest\_16\_0\_3.0 \\ \hline
0.6956 & [PERSON]\_Square\_Eigenvalue\_ & 0.5789 &
\begin{tabular}{c} [PERSON]\_Normalized\_Eigenvalue\_ \\ Difference\_32.0 \\ \end{tabular} \\ \hline
0.6948 & [PERSON]\_1\_2\_0\_2\_0\_0\_2.0 & 0.5717 & [PERSON]\_Determinant\_16.0 \\ \hline
0.6935 & [PERSON]\_Eigenvalue\_1\_32.0 & 0.5614 & Mean\_32.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ranking of attributes using the InfoGainAttributeEvaluation in the WEKA software.