## 3 Results  
Figure 4 shows the performance of each image processing approach with respect to radiological changes. The original DIR images are presented in the top row, next to the same images after histogram equalization transformation. As shown, despite the images gaining better contrast, the visibility of changes did not improve substantially. In the next row in Figure 4, the region where changes existed is enclosed within a red line. In the following rows, the texture feature maps computed for CLU, FOF energy, GTDM busyness, (HEQ) RLM, short run high gray level run emphasis, and LBP are given. Recognition of the borders of the lesions and their internal structure was markedly improved in comparison with the initial DIR image. In the FOF group, the contours of lytic changes were represented effectively. LBP and CLU revealed previously hidden information about the internal structure of the lesions and tissue contrast. (HEQ) RLM was found to improve visualization of lytic and scleroic lesions markedly. The delineation of scleroic lesions and internal pattern recognition were achieved with CLU, RLM, and LBP texture feature maps. The potential utility of each method was calculated based on data derived from experts, and changes are expressed as sensitivity and specificity for the different groups of texture feature maps that are gathered in the bar plots presented in Figure 5. All samples presenting lesions and marked as such by experts take place for true positive (TP) cases. When there was a change unnoticed by the expert, there was a true negative (TN) result. Then, when the expert noticed the change in the DIR data without lesions, a false positive (FP) result was recorded. False negative (FN) results corresponded to the situation in which the data presented any changes, and the expert confirmed it. Consequently, the formulas for sensitivity and specificity are as follows:  
\[Sensitivity=\ \frac{TP}{TP+FN},\]  
\[Specificity=\ \frac{TN}{TN+FP}.\]  
Figure 3: Example of assessment of a periapical lesion. (**a**) Entry digital intraoral radiographic image. (**b**) Differentiation of radiodensity in the lesion (different shapes are pointed out by the arrows). (**c**) Border definition (pointed out by small arrows). (**d**) Tissue contrast between lesion and the neighborhood (shown by the arrows). A very low percentage value meant that the transformation did not improve visibility of a lesion, while a high percentage value indicated improved visibility and a marked increase in potential lesion recognition after texture map utilization in comparison to DIR. Performance of each image processing approach with respect to radiological changes is presented on separate graphs in Figure 5, illustrating the sensitivity and specificity of the proposed methods. Recognition and differentiation of the lytic lesions after use of the texture feature maps showed the highest sensitivity for the (HEQ) RLM texture feature map, with scores of 94%, 89%, and 94% for radiodensity, border definition, and tissue contrast, respectively; specificity for recognition of these parameters was 86%, 89%, and 43%. The next best performing texture feature map was CLU, with a sensitivity of 83%, 77%, and 80%, and a specificity of 74%, 97%, and 51% for radiodensity, border definition, and tissue contrast, respectively. FOF texture feature maps showed relatively low sensitivity for lytic lesions (60%, 69%, and 51%) but high specificity (94%, 91%, and 69%) for recognition of the three chosen radiological features. For the sclero lesions, the (HEQ) RLM texture feature map was again found to have the best performance, with a sensitivity of 97%, 80%, and 97% for recognition of radiodensity, border definition, and tissue contrast, respectively. The specificity of the following texture feature maps was lower for recognition of radiodensity changes (47%) but better for border definition (90%) and tissue contrast differentiation (53%).