## 4 Discussion
Another study assessed the treatment effectiveness of guided bone regeneration in cases of post-resectal and post-cystal bone loss on DIR images obtained using RVG 6100 digital radiography equipment (Kodak) [16]. Fractal dimension measurements (power spectral density, triangular prism surface area, \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Sensitivity** & **Specificity** & **F1** & **Accuracy** \\ \hline \multirow{3}{*}{CLU} & radiodensity & 0.8286 & 0.7429 & 0.7945 & 0.7857 \\  & border def & 0.7714 & 0.9714 & 0.8571 & 0.8714 \\  & tissue contr & 0.8000 & 0.5143 & 0.7000 & 0.6571 \\ \hline \multirow{3}{*}{FOF} & radiodensity & 0.6000 & 0.9429 & 0.7241 & 0.7714 \\  & border def & 0.6857 & 0.9143 & 0.7742 & 0.8000 \\  & tissue contr & 0.5143 & 0.6857 & 0.5625 & 0.6000 \\ \hline \multirow{3}{*}{GTDM} & radiodensity & 0.3143 & 0.8857 & 0.4400 & 0.6000 \\  & border def & 0.3714 & 0.8571 & 0.4906 & 0.6143 \\  & tissue contr & 0.2857 & 0.8000 & 0.3846 & 0.5429 \\ \hline \multirow{3}{*}{LBP} & radiodensity & 0.5143 & 0.6857 & 0.5625 & 0.6000 \\  & border def & 0.1714 & 0.8571 & 0.2609 & 0.5143 \\ \cline{1-1}  & tissue contr & 0.3714 & 0.5143 & 0.4000 & 0.4429 \\ \hline \multirow{3}{*}{HISTEQ-RLM} & radiodensity & 0.9429 & 0.8571 & 0.9041 & 0.9000 \\ \cline{1-1}  & border def & 0.8857 & 0.8857 & 0.8857 & 0.8857 \\ \cline{1-1}  & tissue contr & 0.9429 & 0.4286 & 0.7500 & 0.6857 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensitivity, specificity, F1 score, and accuracy in the differentiation of the different diagnostic parameters of the lytic lesions after use of texture feature maps. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Sensitivity** & **Specificity** & **F1** & **Accuracy** \\ \hline \multirow{3}{*}{CLU} & radiodensity & 0.6000 & 0.5333 & 0.5806 & 0.5667 \\  & border def & 0.6000 & 0.5333 & 0.5806 & 0.5667 \\  & tissue contr & 0.0333 & 1.0000 & 0.0645 & 0.5167 \\  & radiodensity & 0.7333 & 0.6000 & 0.6875 & 0.6667 \\ FOF & border def & 0.7000 & 0.8333 & 0.7500 & 0.7667 \\  & tissue contr & 0.5667 & 0.7333 & 0.6182 & 0.6500 \\  & radiodensity & 0.6333 & 0.6333 & 0.6333 & 0.6333 \\ GTDM & border def & 0.3667 & 0.9333 & 0.5116 & 0.6500 \\  & tissue contr & 0.5000 & 0.7667 & 0.5769 & 0.6333 \\  & radiodensity & 0.7000 & 0.4667 & 0.6269 & 0.5833 \\ LBP & border def & 0.4000 & 0.8333 & 0.5106 & 0.6167 \\  & tissue contr & 0.5333 & 0.6667 & 0.5714 & 0.6000 \\  & radiodensity & 0.9667 & 0.4667 & 0.7733 & 0.7167 \\ HISTEQ-RLM & border def & 0.8000 & 0.9000 & 0.8421 & 0.8500 \\  & tissue contr & 0.9667 & 0.5333 & 0.7945 & 0.7500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensitivity, specificity, F1 score, and accuracy in the differentiation of the different diagnostic parameters of the sclerotic lesions after use of texture feature maps. blanket, intensity difference scaling, and variogram methods) were performed, and the images became smoother during the healing process after bone loss [35]. Despite the progress to date in the quality of plain radiographic images, DIR still has a number of limitations. In addition to \"anatomical noise\" and small differences in bone density, there are other phenomena that impede image quality and hinder the anatomical recognition of potentially pathological structures [36; 37]. Current technical developments in informatics hardware have made it possible to perform very complicated calculations in a relatively short time at low cost. The approach used in the current study provides significantly more radiological information than standard DIR images. This comprehensive study is the first where usability of such a broad set of approaches to image texture analysis was presented. Based on our findings, from a radiological perspective, we consider these techniques a step forward in the recognition and precise localization of periapical cystic lesions.