In recent years, dental image processing has become a valuable diagnostic help for medical practitioners. Due to the non-uniform nature of dental X-rays, reliable diagnoses remain troublesome despite advancements in the area. This is due to the fact that existing systems diagnose caries using a deterministic approach based on a supervised learning model. Using an unsupervised learning model, this code is an algorithm based on an article which is named Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays It proposes a method for detecting caries across a range of non-uniform X-ray pictures. This technique seeks to discover probable caries characteristics within a tooth without comparing it against a set of image-based criteria. The results demonstrate the practicality of an unsupervised learning strategy and the method's superiority over supervised alternatives.

if you use any part of this code, please cite to Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

• Segmentation

  • Pre-processing

  • Thresholding

  • Tooth Separation

• Boundary Detection and Feature Extraction

  • Top and Bottom Hat Transformations

  • Active Contour

• Caries Detection