Dental radiography image enhancement for treatment evaluation through digital image processing

Area of Science:

• Dentistry
• Digital Image Processing
• Medical Imaging

Background:

• Dental periapical radiography is crucial for evaluating treatment outcomes.
• Low-quality radiographic images hinder accurate diagnosis due to noise and poor contrast.
• Improving image quality is essential for effective pulp capping treatment assessment.

Purpose of the Study:

• To enhance the quality of dental radiographic images.
• To improve the evaluation of pulp capping treatments.
• To compare various digital image processing methods for dental radiography.

Main Methods:

• Collected radiographic image data from The Dental Hospital UMY.
• Applied and compared image enhancement techniques: contourlet transform (CT), wavelet transform, contrast stretching (CS), and contrast limited adaptive histogram equalization (CLAHE).
• Evaluated methods based on noise reduction, contrast optimization, and brightness enhancement using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

Main Results:

• Contourlet transform (CT) achieved the lowest Mean Square Error (MSE) of 5.44 and the highest Peak Signal to Noise Ratio (PSNR) of 40.54.
• CLAHE, CS, and Wavelet transform methods showed progressively higher MSE and lower PSNR values.
• CT demonstrated superior performance in noise reduction and image quality enhancement compared to other tested methods.

Conclusions:

• Contourlet transform (CT) is a highly effective method for enhancing dental radiographic images.
• While quantitative metrics (MSE, PSNR) are valuable, qualitative assessment by dentists is necessary for final method selection.
• Optimized dental radiography images can significantly aid in pulp capping treatment evaluation.