Identification of pulpitis at dental X-ray periapical radiography based on edge detection, texture description and artificial neural networks

Abstract

Objectives: The aim of the present research was to identify pulpitis through periapical radiography by applying edges as basis image features, the texture description and the artificial neural networks (ANNs). Materials and Methods: Input image data records of 10 molar and 10 canine teeth were used. The clinical diagnosis of interest cases were represented as normal pulp, reversible and irreversible pulpitis, and necrotic pulp. The following image processing steps were done. First, the data records were converted digitally and preprocessed as its original image using the Gaussian Filter to obtain the best smoothed intensity distribution. Second, the local image differentiation was used to produce edge detector operators, e(x,y) as the image gradient; ∇f(x,y) providing useful information about the local intensity variations. Third, these results were analyzed by using the texture descriptors to obtain digitally the image entropy, H. The fourth step, all were characterized by the ANNs. Results: The edge detection carried important information about the object boundaries of pulpal health and pain conditions in the dental pulp significantly. The image entropy which was identified, the diagnostic term, was obtained from texture descriptors in the segmentation regions where the curves of pulp states tent convergence with the normal pulp line from 4.9014 to 4.6843 decreasing to the reversible and the irreversible pulpitis line include the nectrotic pulp line from 4.6812 to 4.5926 and then inputting to the ANNs analysis at the same of mean square error around 0.0003. Conclusions: Referred to these results, the correlation of the image entropy and the ANNs analysis could be linearly classified with the critical point of 4.6827. Finally, it could be concluded that the direct reading radiography is better to be digitized in order to provide us the best choice for diagnosis validation.