Automatic matching of computed tomography and stereolithography data. (July 2019)

• Record Type: Journal Article
• Authors: Woo, S.; Lee, S.; Chae, J.; Rim, J.; Lee, J.; Seo, J.; Lee, C.
• Is Part Of: Computer methods and programs in biomedicine. Volume 175(2019)
• Page Start: 215
• Page End: 222
• Publication Date: 2019-07
• Subjects: Surgical guidance/navigation -- Tooth -- Optical imaging/oct/dot -- Registration -- X-ray imaging and computed tomography; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.04.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In this paper, an automatic algorithm is proposed for CT and STL registration. 2D images including depth maps were generated to perform the matching process. Then, fine tuning was performed based on volume matching. The proposed method provided average matching error of 2.5 mm within 19 s. Abstract: Background and Objective: Computed tomography (CT) is one of the most frequently used medical imaging methods. An important application area of CT is dental implants, which require precise inspection and analysis of oral structures. Since CT provides a precise 3D model of the teeth, bones and nerves, it can be used as a surgical guide for dental implants. Along with CT, optical 3D images called stereolithography (STL) have also been widely used. STL images obtained from optical 3D images can be used to show the 3D surfaces of oral structures. Since CT data and STL data deploy different technologies to obtain dental information, we can obtain more accurate dental implants by combining the two datasets. Since the two datasets are acquired by using different sensors, the datasets need to be registered. Methods: An automatic matching algorithm is proposed for CT and STL image registration, which is based on depth maps and maximum intensity projection. Then, fine tuning was performed based on volume matching. Results: When applied to real-world databases, the proposed method provided an average matching error of 2.7 mm for the upper jaw and 2.3 mm for the lower jaw with an average processing time of about 19 s. Conclusions: The proposed method performs accurate registration of CT and STL. Graphical abstract: Image, graphical abstract