# Lung Field Segmentation in Chest X-rays:
## 2 Materials and Methods  
### Proposed Segmentation Method  
The proposed method is able to adaptively run per each lung field (right and left) and achieve segmentation in two steps (for the availability of the code see Appendix A). The adaptability of the initially identified region is ensured by automatically selecting rib cage samples, most probably located in the lung area and some support points on the lung border, by incorporating universal a priori  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Main Purpose** & **Main Methodology** & **Comments** \\ \hline \multirow{4}{*}{[PERSON] et al. (2017)} & Assess how clavicles and rib shadows affect lung segmentation & \begin{tabular}{c} UNet-based \\ convolutional neural \\ network \\ \end{tabular} & \begin{tabular}{c} Improved accuracy by using a \\ preprocessed version of the JRT \\ dataset without clavicles and rib \\ shadows. Process is sped up by \\ running on a GPU. \\ \end{tabular} \\ \hline \multirow{2}{*}{[PERSON] et al. (2018)} & Posterior rib segmentation & Iterated contextual pixel classification & \begin{tabular}{c} Evaluated on the JRT dataset. \\ Misclassifications appear in a \\ structured way. \\ \end{tabular} \\ \hline \multirow{2}{*}{[PERSON] et al. (2019)} & Rib recognition & \begin{tabular}{c} Template matching, \\ graph theory and \\ machine learning \\ \end{tabular} & \begin{tabular}{c} Evaluated on the normal X-rays \\ of the JRT dataset. Overlap \\ with clavicle introduces \\ recognition problems. \\ High sensitivity and specificity. \\ \end{tabular} \\ \hline \multirow{2}{*}{[PERSON] et al. (2010)} & Rib segmentation and anatomical labeling & Mask R-CNN & First approach for simultaneous \\  & & \begin{tabular}{c} rib detection and segmentation. \\ Improved detection rate. \\ \end{tabular} \\ \hline \multirow{2}{*}{[PERSON] et al. (2011)} & Eliminate the ribs & \
\begin{tabular}{c} Hough transform and \\ dynamic programming \\ \end{tabular} &
\begin{tabular}{c} Very high sensitivity and \\ specificity. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of approaches for the analysis of lung bone structures found in published literature. The adopted strategy varies and is either the accurate delineation of bone structures for individual analysis or their elimination while segmenting the lung field.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Main Methodology** & **Datasets** & **DSC** & \(\mathbf{\Omega}\) \\ \hline \multirow{2}{*}{[PERSON] et al. (2021)1} & Oriented Gaussian derivatives filter and Fuzzy C-Means & X-ray datasets from different machine types & - & 0.69–0.87 \\ \hline \multirow{2}{*}{[PERSON] et al. (2023)2} & Selective thresholding and ASM & \
\begin{tabular}{c} Portable chest radiographs \\ of patients with bacterial \\ pulmonary infections \\ \end{tabular} & - & 0.91–0.92 \\ \hline \multirow{2}{*}{[PERSON] et al. (2014)3} & Gradient Vector Flow-based ASM & JSRT and CXR & - & 0.84–0.9 \\ \hline \multirow{2}{*}{[PERSON] et al. (2025)} & Active contours; low-level features at boundary &
\begin{tabular}{c} Shanghai Pulmonary \\ Hospital and other clinical \\ sites in China \\ \end{tabular} & 0.88 & - \\ \hline \end{tabular}  
* Score range from worst to best performing dataset.2 Average score for left and right lungs.3 In the paper, scores are given as averages for left and right lungs, for each evaluated dataset. \(\Omega\): Jaccard similarity coefficient, DSC: Dice Similarity Coefficient (see Results section).  
\end{table}
Table 2: Summary of related lung segmentation approaches.  
knowledge. The selection of points does not require any previous training, and it will be proven, where necessary, that these points can be indicative of features in challenging segmentation cases.