Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

摘要

In recent years dental image processing has become a useful tool in aiding healthcare professionals diagnose patients. Despite advances in the field, accurate diagnoses are still problematic due to the non-uniform nature of dental X-rays. This is attributed to current systems utilizing a supervised learning model for their deterministic algorithm when identifying caries. This paper presents a method for the detection of caries across a variety of non-uniform X-ray images using an unsupervised learning model. This method aims to identify potential caries hallmarks within a tooth without comparing against a set of criteria learned from a database of images. The results show the viability of an unsupervised learning approach and the effectiveness of the method when compared to the supervised approaches.