Selection of the optimum features to identify tooth decay in the panoramic images based on image texture analysis

Abstract

The process of identifying pathological patterns in dental radiographic images (panorama images) is one of the most important stages of diagnosing diseases for dentists, and in light of the tremendous technological development, especially in the field of machine learning and pattern recognition, the Digital Image processing department has the most important role in the field of image fragmentation Extract the necessary features in order to identify pathological patterns and thus easily extract the pathological features of the input images. In this research, a methodology has been proposed to extract the features related to tooth decay from the digital Panorama radiographs obtained from the VaTech 400 device using image texture analysis based on the gray level co-occurrence matrix (GLCM) algorithm where the digital image was first entered into the computer and then converted to the Gray level, processed and noise removal facilities for the extraction process and then the statistical features of the GLCM matrix were extracted and then the choice of optimum features that lead to improved decay detection. The obtained results are shown increasing of accuracy of the results and improve the diagnosis process.