## 5 Results and Discussion
### Preparation Stage Results  
Based on the adopted multilevel thresholding procedure, a binary mask is generated for each slice, as shown in Figure 6.  
The mask images clearly outline the lung tissue in the middle lung slices and manifest a constant image for peripheral lung slices. The presented output manifest the success of the thresholding approach in generating segmentation masks for the lung tissue. Dice coefficient is calculated for the three datasets and the results are shown in Table 3. The achieved \(D_{C}\) values presents acceptable performance as the recommended \(D_{C}\) value for a good overlap is \(>\)0.700 according to [PERSON] et al. [37]. Hence, the HU-based mask generation approach is considered satisfactory, especially that the masks are used solely for textural analysis and subsequent slice selection.  
For each slice \(S\), \(ASM\), \(C\), \(SE\), \(H\), \(E\), \(Ent_{H}\), \(K_{H}\) and \(Sk_{H}\) features are extracted from the full HU CT slices and their corresponding binary masks \(S_{LP}\). In Figure 7, Haralick features values variation across slices of five patients are depicted. The diagrams elucidate that the features generated from the binary masks between peripheral slices and middle slices better differentiate discriminative vs non-discriminative features. For instance, \(ASM\) takes a value of one for constant images denoting non-discriminative lung slices. Similarly, contrast feature \(C\) has a constant value of zero for all peripheral non-informative lung slices, while it varies with the middle informative lung slices. Histogram based features depict the same pattern, which is clear when comparing Figures 8 and 9. Thus, the features extracted from the binary masks are the ones selected to be input to the clustering algorithm.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & \(D_{C}\) \\ \hline NSCLC Radiomics (Lung 1) & 0.79 \\ NSCLC Radiomics-Genomics & 0.85 \\ NSCLC Radio-Genomics & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: HU-based segmentation evaluation using dice (\(D_{C}\)) coefficient on the TCIA NSCLC datasets.  
Figure 6: Output of HU multilevel thresholding from five different patients (CT Scans) at five different positions (First Slice, two middle slices, Slice 250, and last Slice).  
The performance of modified k-means variant [30], agglomerative [31], Spectral [32], and BIRCH [33] clustering is evaluated. Figure 10 shows the Sil index and DB index of each clustering algorithm varying with cluster number. Considering Sil index, k-means  
Figure 7: Haralick Textural Features extracted from lung tissue binary mask slices (**left**) and from full HU band CT slices (**right**).  
presents the best performance across all cluster numbers. While in terms of DB, it is equivalent to BIRCH clustering at two clusters. Nevertheless, k-means is employed for the purpose of slice selection for its intrinsic advantages such as scalability to large datasets and adaptiveness to data points cluster assignment [41].  
Figure 8: Histogram-based Features extracted from Lung tissue binary mask slices.  
The best number of clusters is decided through the elbow method, as illustrated in Figure 11. From the shown diagrams, the optimum number of clusters can be chosen to  
Figure 9: Histogram-based Features extracted from Full HU Bands CT slices.  
be two or three. However, in view of the Sil and DB indices values in Figure 10, we opt to two clusters. The choice of two clusters also naturally corresponds to the open/closed lung partitions. Given the resultant clusters, the \(n\) slices nearest to the cluster centroid are selected per patient. The chosen slices are used to construct the 3D volumetric structures (nifti files), which are input to ALT-CNN-DENSE _Net_ for training, validation and testing.