牙科全景射线照片用于计算机辅助图像分析，通过分析生成的图像能力来识别强度波动模式，从而检测异常组织块。这样做是为了减少诊断所需的侵入性活检的需要。当前研究的目的是检查和比较几种纹理分析技术的准确性，例如灰度游程矩阵（GLRLM）、灰度共生矩阵（GLCM）和小波分析在识别牙囊肿、肿瘤、本次回顾性研究共检索了172张牙科全景片，病变包括牙囊肿、肿瘤或脓肿。不符合诊断质量技术标准（例如牙齿明显重叠、弥散图像或变形）的射线照片被排除在样本之外。该研究采用的方法包括五个阶段。首先，改进了射线照片，并手动分割了感兴趣的区域。使用多种特征提取技术，例如 GLCM、GLRLM 和小波分析来收集感兴趣区域的信息。随后，将病变分类为囊肿、肿瘤、脓肿，或者使用支持向量机（SVM）分类器。最终，数据被转移到 Microsoft Excel 电子表格中，并使用社会科学统计软件包 (SPSS)（版本 21）进行统计分析。最初计算描述性统计数据。对于推断分析，统计显着性由 p 值 < 0.05 确定。使用敏感性、特异性和准确性来发现评估诊断与实际诊断之间的显着差异。研究结果表明，使用 GLCM 可以达到 98% 的准确性，使用小波分析可以达到 91% 的准确性

Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions.The current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis.The findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM.The GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.© 2023. The Author(s).