# The Use of a 3D Image Comparison Program for Dental Identification
## 2 Materials and Methods  
Volunteers were recruited at Kanagawa Dental University Hospital, and 20 subjects with more than 20 teeth were recruited. Plaster casts of the maxilla and mandible were obtained from the subjects after informed consent. STL data were acquired by the three dentists using the TRIOS(r) intraoral scanner (3 Shape, Copenhagen, Denmark) from the maxillary and mandibular models of 20 subjects (model numbers 1-20) with more than 20 teeth. The acquired STL data were trimmed using Autodesk Meshmixer(r) (Autodesk, Tokyo, Japan) for the left lateral dentition only, and the plane formed by two points at the proximal and distal corners of the central incisors, and the midpoint of the central limb ridge of the most posterior tooth was defined as the reference plane. For the maxilla, the plane with the reference point 2.5 mm vertically down from the reference plane toward the root was used as the cross-section. For the mandible, a plane with a reference point 2.5 mm vertically down from the reference plane toward the root for anterior teeth and 1.5 mm for posterior teeth was used as the cross-sectional plane. The sliced cross-sectional images were binarized in Adobe Photoshop(r) (Adobe, San Jose, CA, USA) to produce a total of 120 binarized cross-sectional images, 60 each for the maxillary and mandibular models. Figure 1 shows the procedure used to generate the binarized images. On a separate day, one of the three dentists read all 20 models and generated images in the same manner as above, generating 20 images each for the maxillary and mandibular model sections, for a total of 40 images. The images generated by these two methods were used to analyze the discrimination ability. The same maxillary model was read and imaged by the three dentists once a day for seven days, and 21 images were generated from the same model using the same method. To compare these 21 images, one image was taken on a different day from the same model using the same criteria. These two images were used to evaluate the differences between the practitioners. Figure 1: Binarized image creation procedure. In order, from the left image: 1—Captured STL data displayed in Autodesk Meshmixer. 2—STL data sliced by the length set from the reference point in Autodesk Meshmixer. 3—Sectional image of the sliced data. 4—Binarized sliced image with intermediate values using Adobe Photoshop. 5—Binarized sliced image using Adobe Photoshop. 6—Sectional image of the sectioned data. Although there are various methods for image comparison, the AKAZE method was used in this study. In this study, we implemented and used an image comparison program based on the AKAZE method using OPEN CV and Python. The AKAZE method is an algorithm that can detect total features in a short time while maintaining high recognition accuracy. It is superior to other methods, such as SIFT (Scale-Invariant Feature Transformer) [25] and SURF (Speeded-Up Robust Features) [30], in terms of processing speed and has properties that are resistant to rotation and expansion. The AKAZE method can determine whether objects photographed from different angles are identical, can compare and determine whether the same object appears in two images by calculating their feature values, and can quantify the degree of similarity between images by comparing their calculated values. Figure 2 shows an image comparison using AKAZE. AKAZE can be used free of charge because there is no patent approval, and the software implementation is relatively simple [26]. In this study, the AKAZE value was defined as the average value of the Hamming distance of the feature values between binary images, where the lower the value calculated by brute-force matching of the feature points detected by AKAZE is, the more similar the images are.