Automatic Image Slice Marking Propagation on Segmentation of Dental CBCT
Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. CBCT tooth segmentation has many challenges such as low contrast, boundaries of blurred teeth and irregular contours of the teeth. In addition, because the CBCT results produce a lot of slices, where each slice has information that is related and the topology of each slice can differ from one another, so the marking on each slice becomes exhaustive and inefficient, due to information similarities on each slice. In this study, we propose an automatic image slice marking propagation on segmentation of dental CBCT. Marker from the result of the first slice segmentation will be use as the information for the next slices propagately. This study was successful in segmenting using the proposed marking method with an error value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.
B. Studebaker, L. Hollender, L. Mancl, J. D. Johnson, and A. Paranjpe, “The Incidence of Second Mesiobuccal Canals Located in Maxillary Molars with the Aid of Cone-beam Computed Tomography,” J. Endod., vol. 44, no. 4, pp. 565–570, Apr. 2018.
S. Patel, A. Dawood, T. P. Ford, and E. Whaites, “The potential applications of cone beam computed tomography in the management of endodontic problems,” Int. Endod. J., vol. 40, no. 10, pp. 818–830, Oct. 2007.
G. John, T. Joy, J. Mathew, and V. B. Kumar, “Applications of cone beam computed tomography for a prosthodontist,” J. Indian Prosthodont. Soc., vol. 16, no. 1, p. 3, 2016.
L. R.J., P. S., P. J., N. G., H. D., and O. S., “Three-dimensional evaluation of root position at the reset appointment without radiographs: a proof-of-concept study,” Prog. Orthod., vol. 19, no. 1, 2018.
M. Kumar, M. Shanavas, A. Sidappa, and M. Kiran, “Cone beam computed tomography - know its secrets.,” J. Int. oral Heal. JIOH, vol. 7, no. 2, pp. 64–8, 2015.
“Ketabi et al. 2019 - Detection and measurements of apical lesions in th ... phy and panoramic radiography as a function of cortical bone thickness.pdf.” .
A. Aminoshariae, J. C. Kulild, and A. Syed, “Cone-beam Computed Tomography Compared with Intraoral Radiographic Lesions in Endodontic Outcome Studies: A Systematic Review.,” J. Endod., vol. 44, no. 11, pp. 1626–1631, Nov. 2018.
S. Patel, J. Brown, T. Pimentel, R. D. Kelly, F. Abella, and C. Durack, “Cone beam computed tomography in Endodontics – a review of the literature,” Int. Endod. J., pp. 1–15, 2019.
G. Rodríguez, F. Abella, F. Durán-Sindreu, S. Patel, and M. Roig, “Influence of Cone-beam Computed Tomography in Clinical Decision Making among Specialists,” J. Endod., vol. 43, no. 2, pp. 194–199, Feb. 2017.
G. Rodríguez, S. Patel, F. Durán-Sindreu, M. Roig, and F. Abella, “Influence of Cone-beam Computed Tomography on Endodontic Retreatment Strategies among General Dental Practitioners and Endodontists,” J. Endod., vol. 43, no. 9, pp. 1433–1437, Sep. 2017.
J. Nilsson, R. G. Richards, A. Thor, and L. Kamer, “Virtual bite registration using intraoral digital scanning, CT and CBCT: In vitro evaluation of a new method and its implication for orthognathic surgery,” J. Cranio-Maxillofacial Surg., vol. 44, no. 9, pp. 1194–1200, Sep. 2016.
S. Kakehbaraei, H. Seyedarabi, and A. T. Zenouz, “Dental Segmentation in Cone-beam Computed Tomography Images Using Watershed and Morphology Operators.,” J. Med. Signals Sens., vol. 8, no. 2, pp. 119–124.
J. Lv, F. Wang, L. Xu, Z. Ma, and B. Yang, “A segmentation method of bagged green apple image,” Sci. Hortic. (Amsterdam)., vol. 246, no. November 2018, pp. 411–417, 2019.
Y. Fan et al., “Marker-based watershed transform method for fully automatic mandibular segmentation from CBCT images,” Dentomaxillofacial Radiol., vol. 48, no. 2, p. 20180261, Feb. 2019.
“Gao and Chae 2010 - Individual tooth segmentation from CT images using level set method with shape and intensity prior.pdf.” .
“Xia et al. 2017 - Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth.pdf.” .
“Gan et al. 2018 - Tooth and Alveolar Bone Segmentation From Dental Computed Tomography Images.pdf.” .
A. Z. Arifin, Maryamah, S. Arifiani, A. Fariza, D. A. Navastara, and R. Indraswari, “Hierarchical Clustering Linkage for Region Merging in Interactive Image Segmentation on Dental Cone Beam Computed Tomography,” 2018 Int. Conf. Appl. Inf. Technol. Innov., pp. 124–128, 2018.
L. Wang, J. Li, Z. Ge, and G. Li, “CBCT image based segmentation method for tooth pulp cavity region extraction,” Dentomaxillofacial Radiol., vol. 48, no. 2, p. 20180236, Feb. 2019.
S. S. Naumovich, S. A. Naumovich, and V. G. Goncharenko, “Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation,” Dentomaxillofacial Radiol., vol. 44, no. 4, pp. 1–6, 2015.
R. Indraswari, T. Kurita, A. Z. Arifin, N. Suciati, E. R. Astuti, and D. A. Navastara, “3D Region Merging for Segmentation of Teeth on Cone-Beam Computed Tomography Images,” 2018 Jt. 10th Int. Conf. Soft Comput. Intell. Syst. 19th Int. Symp. Adv. Intell. Syst., pp. 389–393, 2018.
C. Y., “Mean Shift, Mode Seeking and Clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 8, pp. 790–799, 1995.
D. Comaniciu, P. Meer, and S. Member, “Mean Shift: A Robust Approach Toward Feature Space Analysis,” vol. 24, no. 5, pp. 603–619, 2002.
N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man Cybern., vol. 20, no. 1, pp. 62–66, 1979.