Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
Dental Students' Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
Oral Radiol. 2023 Apr;39(2):418-424. doi: 10.1007/s11282-022-00654-7. Epub 2022 Sep 8.
This study aims to evaluate the effects of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) on periapical images and fractal dimensions in the periapical region.
In this cross-sectional study, digital periapical images were selected from the archive of Dentistry School of Isfahan University of Medical Sciences. The radiographs were taken from mandibular and maxillary anterior single root teeth with healthy root and periodontium. After applying HE and CLAHE algorithms to images, two radiologists evaluated the quality of apex detection from using a 5-point Likert scale (from 5 for very good image quality to 1 for very bad image quality). Afterward, all the images were imported to the ImageJ application, and the region of interest (ROI) was specified as the region between the two central incisors. The fractal box-counting method was used to determine fractal dimensions (FD) values. Nonparametric Wilcoxon-Friedman test, Intraclass Correlation Coefficient test, T-test, and Pair T-test were performed as statistical analysis (α = 0.05).
Fifty-three radiographs were analyzed and the image quality assessments were significantly different between raw images and images after performing HE, CLAHE (p value < 0.001), and using CLAHE algorithm significantly increases image quality assessments more than HE (p value = 0.009). There was a significant difference in FD values for images after applying CLAHE and HE compared to raw images (p value < 0.001), and HE decreased the FD value significantly more than CLAHE (p value = 0.019).
Employing CLAHE and HE algorithm via OpenCV python library improves the periapical image quality, which is more significant using the CLAHE algorithm. Moreover, applying CLAHE and HE reduces trabecular bone structure detection and FD values in periapical images, especially in HE.
本研究旨在评估直方图均衡化（HE）和限制对比度自适应直方图均衡化（CLAHE）对根尖图像和根尖区域分形维数的影响。
在这项横断面研究中，从伊斯法罕医科大学牙科学院的档案中选择了数字根尖图像。射线照片取自下颌和上颌前单根牙齿，根和牙周健康。对图像应用 HE 和 CLAHE 算法后，两位放射科医生使用 5 分李克特量表（从 5 分表示非常好的图像质量到 1 分表示非常差的图像质量）评估根尖检测质量。之后，将所有图像导入 ImageJ 应用程序，并将感兴趣区域（ROI）指定为两个中切牙之间的区域。使用分形盒计数法确定分形维数（FD）值。进行非参数 Wilcoxon-Friedman 检验、组内相关系数检验、t 检验和配对 t 检验作为统计分析（α=0.05）。
分析了 53 张射线照片，原始图像与应用 HE、CLAHE 后的图像之间的图像质量评估差异显著（p 值<0.001），并且使用 CLAHE 算法比 HE 显著增加图像质量评估（p 值=0.009）。与原始图像相比，应用 CLAHE 和 HE 后的 FD 值图像有显著差异（p 值<0.001），HE 比 CLAHE 显著降低 FD 值（p 值=0.019）。
通过 OpenCV python 库应用 CLAHE 和 HE 算法可以提高根尖图像质量，使用 CLAHE 算法效果更显著。此外，应用 CLAHE 和 HE 降低了根尖图像中小梁骨结构检测和 FD 值，尤其是在 HE 中。
