@article {10.3844/jcssp.2025.2523.2529,
article_type = {journal},
title = {Performance Analysis of Modified Enhancement Method for Dental Images - Towards the Effective Detection of Dental Disorders},
author = {R, Krishnaveni and R, Sudarmani},
volume = {21},
number = {11},
year = {2025},
month = {Dec},
pages = {2523-2529},
doi = {10.3844/jcssp.2025.2523.2529},
url = {https://thescipub.com/abstract/jcssp.2025.2523.2529},
abstract = {Dental health maintenance is essential for overall physiological well-being, and X-ray imaging remains a fundamental diagnostic tool for comprehensive dental examination. While direct clinical observation by dentists is primary, computer-aided diagnostic tools serve as valuable secondary readers, enhancing diagnostic accuracy and efficiency. This study proposes and evaluates the Gaussian CLAHE Enhancement (GCE) method, which combines Gaussian filtering (GF) with Contrast Limited Adaptive Histogram Equalization (CLAHE) for dental radiograph enhancement. The proposed GCE method demonstrates superior performance in improving dental image clarity while maintaining low computational complexity. Comparative analysis against existing dental image enhancement techniques reveals significant improvements across multiple quantitative metrics: Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Contrast Ratio (CR), Structural Similarity Index (SSIM), and Lightness Order Error (LOE). The GCE method achieves an MSE of 0.24, PSNR of 39.351 dB, and enhancement accuracy of 95.83%, representing improvements of 2.61% over the dFDB-LSHADE method, 6.95% over the Ded-Net method, and substantial gains over other state-of-the-art techniques. These results demonstrate the clinical viability of the proposed method as a reliable computer-aided diagnostic tool for dental radiograph enhancement, with potential applications in automated dental diagnosis and treatment planning.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}