Oriented-tooth recognition using a five-axis object-detection approach

X-ray images are essential data sources for checking the condition of the teeth, gums, jaws, and bone structure of the mouth. Tooth recognition is fundamental in image-processing-based diagnoses. In most previous recognition studies, only four-axis-based object-detection models have been considered because they perform normal object detection while the object is resting on a flat surface. However, because the teeth have various orientations, the existing four-axis-based model leads to inaccurate and inefficient recognition results. Thus, in this study, we propose a five-axis-based object-detection model that considers the orientation of the tooth. Based on a tooth-image dataset labeled using the five-axis ground truth, our proposed method processed five-axis annotated data by employing a variant of the faster region-based convolutional neural network. In the experiment, our proposed method outperformed the existing four-axis approach, both qualitatively and quantitatively. The experimental results indicated that the proposed five-axis-based recognition model will be an important basis for a dental-image-based diagnosis.