Segmentation of Teeth Region via Machine Learning in Panoramic X-Ray Dental Images

Abstract

Segmentation of teeth region from the dental panoramic X-Ray images is an important task in determining various diseases. The main goal of this article is to be able to automatically segment the region of teeth in panoramic x-ray images. First, the center point of the teeth area in the images was determined automatically. Then, a feature set was developed including intensity values of pixels, x-coordinate relative to this center point, y-coordinate relative to this point, and the pixel values obtained by subtraction of maximum and minimum values in 3x3 window. CatBoost algorithm was used for machine learning. When creating the machine learning model, k-fold cross validation of training data set and grid search optimization of hyper parameters, were applied to avoid over fitting of data set. The results were analyzed using the learning curve, F1, accuracy, recall, and precision scores.