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Unsupervised caries detection in non-standardized bitewing dental X-Rays.

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In recent years dental image processing has become a useful tool in aiding healthcare professionals diagnose patients by reducing some of the problems inherent with dental radiographs. Despite advances in the eld, accurate diagnoses of dental caries using Comptuer-aided Diagnosis (CAD) tools are still problematic due to the non-uniform nature of dental X-rays. The reason as to why accurate diagnoses are problematic is in part due to exisiting systems utilizing a supervised learning model for their diagnostic algorithms. Using this approach results in a detection system which is trained to identify caries under speci c conditions. When the input images vary greatly from the training set, these systems have a tendency to misdiagnose patients or miss possible caries altogether. A method for the segmentation of teeth in periapical X-Rays is presented in this dissertation as well as a method for the detection of caries across a variety of non-uniform X-ray images using an unsupervised learning model. The diagnostic method proposed in this dissertation uses an assessment protocol similar to how dentists evaluate the presence of caries. Using this assessment protocol results in caries being evaluated relative to the image itself and not evaluated relative to a set of identi ers obtained from a learning model. The viability of an unsupervised learning model, and its relative e ectiveness of accurately diagnosing dental caries when compared to current systems, is indicated by the results detailed in this dissertation. The proposed model achieved a 96% correct diagnostic which proved competitive with existing models.


Master of Science in Computer Science. University of KwaZulu-Natal, Durban 2017.


Theses - Computer Science.