## 4 Discussion  
During image analysis, the observer first notices whole objects, anything that has delineated edges, and anything that is in contrast with the surrounding area. However, when the image quality is very low, the content is blurred, the image has no contrast, or an object's texture may appear very similar to that of the background. In such cases, only analysis of local differences that may be hard to discern on standard radiography may unmask some of the vital information that can be derived from the image. Although it may be impossible to detect differences by assessment of plain radiographic images, transforming visual data derived from an image with algorithms designed to map structural differences may reveal hidden content. The process used in our study is presented in Figure 2 in a sequence of images depicting how a substantial amount of additional information about a lesion was gained in comparison to the initial DIR image. The method of using texture feature maps obtained by texture analysis of the DIR images used in the current study is consistent with that used in some previous studies [19; 20]. Those methods are applicable to wide range of problems e.g., for identification of macroals [28], defect detection [29]. Moreover, there exists a MaZda system [30], which implements some of the described texture feature maps and returns similar results when compared to our implementation. Systems for differentiating cysts, ameloblastomas, and keratocysts on DIR images are described in those reports. The general approach consists of image preprocessing (e.g., opening, contrast stretching), obtaining similarity measures, and texture analysis. In the current study, a much broader set of texture features based on different approaches to image analysis was utilized. FOF feature maps yielded significant improvements in delineation of lytic changes in comparison with DIR. GTDM enhanced visualization of the internal structure within a lytic area and important details of adjacent trabeculation with preserved lesion contours. LBP yielded a surface scene with a clearly differentiated surface pattern at the site of the lytic area. CLU increased tissue contrast in areas of lytic changes. Importantly, (HEQ) RLM increased differentiation of the border and contrast in lytic lesions but did not perform so well for sclerotic changes. Performance of different texture feature maps expressed in terms of sensitivity, specificity, F1 score, and accuracy were summarized in the Table 1 (for the lytic lesions recognition) and the Table 2 (for sclerotic lesions recognition). Figure 6: Relationship between specificity and sensitivity for (**a**) lytic lesions and (**b**) sclerotic lesions. Generally, the higher both values are, the better the parameter. CLU, clustering with k-means approach; FOF, first-order features; GTDM, gray-tone difference matrices; HEQ, histogram equalization; LBP, local binary patterns; RLM, run-length matrices. Few studies of texture analysis have evaluated periapical changes. Possible differentiation of lytic lesions for granulomas and periapical cysts on the basis of radiograms with use of radiometric analysis by histogram calculation and histogram equalization were proposed by [PERSON] and [PERSON], respectively [31; 32]. In another study, after some classic image processing methods (top hat, erosion, and opening) were performed, the skeleton was extracted, and textural features were calculated for the region of interest. A pair of pre-treatment and post-treatment values was then tested in an evaluation of the healing process [33]. The scope of such studies has typically been the detection of areas of alveolar bone on periapical dental radiographs [12; 13; 14; 15]. These considerations were focused on segmentation, detection of lesions, and measuring the degree of alveolar bone loss rather than a textural analysis in cases requiring differentiation of lesions. A similar approach has been reported for the analysis of panoramic images in order to enhance the recognition of caries [34].