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Published on: September 11, 2011
Evaluation of histogram equalization and contrast limited adaptive histogram equalization effect on image quality and
Mojdeh Mehdizadeh1, Kioumars Tavakoli Tafti2, Parisa Soltani1
1Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
Objectives:
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.
Methods:
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).
Results:
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).
Conclusions:
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.
