BACKGROUND: Plaster casts of individual patients are important for orthodontic specialists during the treatment process and their analysis is still a standard diagnostical tool. But the growing capabilities of information technology enable their replacement by digital models obtained by complex scanning systems. METHOD: This paper presents the possibility of using a digital camera as a simple instrument to obtain the set of digital images for analysis and evaluation of the treatment using appropriate mathematical tools of image processing. The methods studied in this paper include the segmentation of overlapping dental bodies and the use of different illumination sources to increase the reliability of the separation process. The circular Hough transform, region growing with multiple seed points, and the convex hull detection method are applied to the segmentation of orthodontic plaster cast images to identify dental arch objects and their sizes. RESULTS: The proposed algorithm presents the methodology of improving the accuracy of segmentation of dental arch components using combined illumination sources. Dental arch parameters and distances between the canines and premolars for different segmentation methods were used as a measure to compare the results obtained. CONCLUSION: A new method of segmentation of overlapping dental arch components using digital records of illuminated plaster casts provides information with the precision required for orthodontic treatment. The distance between corresponding teeth was evaluated with a mean error of 1.38% and the Dice similarity coefficient of the evaluated dental bodies boundaries reached 0.9436 with a false positive rate [Formula: see text] and false negative rate [Formula: see text].

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Zikova 1903 4 166 36 Prague 6 Czech Republic

Department of Computing and Control Engineering University of Chemistry and Technology Prague Technická 5 166 28 Prague 6 Czech Republic

Department of Neurology Charles University Sokolská 581 500 05 Hradec Králové Czech Republic

Department of Paediatric Stomatology The 2nd Medical Faculty Charles University 5 Úvalu 84 150 06 Prague 5 Czech Republic

See more in PubMed

Opheij DG, Opdebeeck H, Steenberghe DV, Quirynen M. Age as compromising factor for implant insertion. Periodontol. 2003;2000(33):172–184. doi: 10.1046/j.0906-6713.2003.03314.x. PubMed DOI

Kasparova M, Grafova L, Dvorak P, Dostalova T, Prochazka A, Eliasova H, et al. Possibility of reconstruction of dental plaster cast from 3D digital study models. Biomed Eng Online. 2013;12(49):1–11. PubMed PMC

Kasparova M, Prochazka A, Grafova L, Yadollahi M, Vysata O, Dostalova T. Evaluation of dental morphometrics during the orthodontic treatment. Biomed Eng Online. 2014;13:1–13. doi: 10.1186/1475-925X-13-68. PubMed DOI PMC

Keating AP, Knox J, Bibb R, Zhurov AI. A comparison of plaster, digital and reconstructed study model accuracy. J Orthod. 2008;35(3):191–201. doi: 10.1179/146531207225022626. PubMed DOI

Ogodescu AS, Sinescu C, Ogodescu EA, Negrutiu M, Bratu E. Digital tools in the interdisciplinary orthodontic treatment of adult patients. NAUN Int J Biol Biomed Eng. 2010;4(3):97–105.

Chang YB, Xia JJ, Gateno J, Xiong Z, Zhou X, Wong STC. An automatic and robust algorithm of reestablishment of digital dental occlusion. IEEE Trans Med Imaging. 2010;29(9):1652–1663. doi: 10.1109/TMI.2010.2049526. PubMed DOI PMC

Yaqi M, Zhongke L. Computer aided orthodontics treatment by virtual segmentation and adjustment. In: International conference on image analysis and signal processing (IASP); 2010. pp. 336–39.

Chapuis J, Schramm A, Pappas I, Hallermann W, Schwenzer-Zimmerer K, Langlotz F, et al. A new system for computer-aided preoperative planning and intraoperative navigation during corrective jaw surgery. IEEE Trans Inf Technol Biomed. 2007;11(3):274–287. doi: 10.1109/TITB.2006.884372. PubMed DOI

Yadollahi M, Prochazka A, Kasparova M, Vysata O. The use of combined illumination in segmentation of orthodontic bodies. Signal Image Video Process. 2015;9(1):243–250. doi: 10.1007/s11760-014-0653-4. DOI

Yamamoto K, Hayashi S, Nishikawa H, Nakamura S, Mikami T. Measurements of dental cast profile and three-dimensional tooth movement during orthodontic treatment. IEEE Trans Biomed Eng. 1991;38(4):360–365. doi: 10.1109/10.133232. PubMed DOI

Bansal GJ. Digital radiography. A comparison with modern conventional imaging. Med J. 2006;82:425–428. PubMed PMC

Kavalcova L, Skaba R, Kyncl A, Rouskova B, Prochazka A. The diagnostic value of MRI fistulogram and MRI distal colostogram in patients with anorectal malformations. J Pediatr Surg. 2013;48(8):1806–1809. doi: 10.1016/j.jpedsurg.2013.06.006. PubMed DOI

Harrell WE, Hatcher DC, Bolt RL. In search of anatomic truth: 3-dimensional digital modeling and the future of orthodontics. Am J Orthod Dentofac Orthop. 2002;122(3):325–330. doi: 10.1067/mod.2002.126147. PubMed DOI

Tomaka A, Tarnawski M, Luchowski L, Lisniewska-Machorowska B. Digital dental models and 3D patient photographs registration for orthodontic documentation and diagnostic purposes. In: Kurzynski M, Puchala E, Wozniak M, Zolnierek A, editors. Computer recognition systems 2. Advances in soft computing. New York: Springer; 2007.

Singh GD. Digital diagnostics: three-dimensional modelling. Br J Oral Maxillofac Surg. 2008;46(1):22–26. doi: 10.1016/j.bjoms.2006.10.008. PubMed DOI

Lindner C, León FP. Model-based segmentation of surfaces using illumination series. IEEE Trans Instrum Meas. 2007;56(4):1340–1346. doi: 10.1109/TIM.2007.899913. DOI

Shinde B, Dani AR. Noise detection and removal filtering techniques in medical images. Int J Eng Res Appl. 2012;2(4):311–316.

Suganthi A, Senthilmurugan M. Comparative study of various impulse noise reduction techniques. Int J Eng Res Appl. 2013;3(5):1302–1306.

Teoh SH, Ibrahim H. Median filtering frameworks for reducing impulse noise from grayscale digital images: a literature survey. Int J Future Comput Commun. 2012;1(4):323–327. doi: 10.7763/IJFCC.2012.V1.87. DOI

Rizon M, Yazid H, Saad P, Shakaff AYM, Saad AR, Sugisaka M. Object detection using circular Hough transform. Am J Appl Sci (2) 2005;2(12):1606–1609. doi: 10.3844/ajassp.2005.1606.1609. DOI

Jain L, Mahor D. Application of Hough transform for finding parametric curves. Int J Comput Appl Eng Sci. 2011;1(2):100–103.

Nitasha SS, Sharma R. Comparison between circular Hough transform and modified cCanny edge detection algorithm for circle detection. Int J Eng Res Technol (IJERT) 2012;1(3):15.

Ghadiri F, Pourreza H, Banaee T, Delgir M. Retinal vessel tortuosity evaluation via circular Hough transform. In: 18th Iranian conference on biomedical engineering, Tehran, Iran. IEEE; 2011. pp. 181–184.

Stanescu A, Burdescu DD. Medical image segmentation—a comparison of two algorithms. In: International conference on medical measurements and applications proceedings. IEEE; 2010

Adams R, Bischof L. Seeded region growing. Int J Pattern Recognit Lett. 1994;16(6):641–647.

Jung C, Liu J, Sun T, Jiao L, Shen Y. Automatic image segmentation using constraint learning and propagation. Digit Signal Process. 2014;24:106–116. doi: 10.1016/j.dsp.2013.09.006. DOI

Iscan Z, Yuksel A, Dokur Z, Korurek M, Olmez T. Medical image segmentation with transform and moment based features and incremental supervised neural network. Digit Signal Process. 2009;19(5):890–901. doi: 10.1016/j.dsp.2009.03.001. DOI

Gonzales RC, Woods RE, Eddins SL. Digital image processing using MATLAB. Upper Saddle River, New Jersey: Pearson Education, Inc.; 2004.

Ansari MA, Anand RS. Region based segmentation and image analysis with application to medical imaging. In: IET-UK international conference on information and communication technology in electrical sciences, Dr. M.G.R. University, Chennai, Tamil Nadu, India; 2007. pp. 724–729.

Kunte A, Bhalchandra A. Effective region based segmentation technique for high resolution aerial imagery. In: Fifth international conference on image and graphics, Xi’an, Shanxi. IEEE; 2009. pp. 272–275.

Praveena SM, IlaVennila D. Optimization fusion approach for image segmentation using k-means algorithm. Int J Comput Appl. 2010;2(7):18–25.

Zhu SC, Yuille A. Region competition: unifying snakes, region growing, and Bayes/MOL for multiband image segmentation. Int J Pattern Anal Mach Intell. 1996;18(9):884–900. doi: 10.1109/34.537343. DOI

Kaganami HG, Beiji Z. Region-based segmentation versus edge detection. In: Fifth international conference on intelligent information hiding and multimedia signal processing, Kyoto. IEEE; 2009. pp. 1217–1221.

Srinivasan GN, Shobha G. Segmentation techniques for ATDR. NAUN Int J Comput. 2008;2(9):165–171.

Böhm C, Kriegel HP. Determining the convex hull in large multidimensional databases, vol. 2114. In: International conference on data warehousing and knowledge discovery. Munich: Springer; 2001. pp. 294–306.

Nagabhushana S. Computer vision and image processing. New York: New Age International; 2009.

Xia R, Wang P, Zhang W, Xiong Q. A novel overlapping mice macrophages images segmentation method. In: International conference on image analysis and signal processing, Linhai. IEEE; 2009. pp. 40–43.

Yang J, Li X. Boundary detection using mathematical morphology. Pattern Recognit Lett. 1995;16(12):1277–1286. doi: 10.1016/0167-8655(95)00082-1. DOI

Stein SC, Schoeler M, Papon J, Worgotter F. Object partitioning using local convexity. In: Conference on computer vision and pattern recognition. IEEE, Columbus; 2014.

Hafizah WM, Soh JZE, Supriyanto E, Nooh SM. Automatic classification of muscle condition based on ultrasound image morphological differences. Int J Biol Biomed Eng. 2012;6(1):87–96.

Zhang XQ, Yang K, Bao-qing H. Cell-edge detection method based on canny algorithm and mathematical morphology. In: International congress on image and signal processing CISP2010. IEEE, Yantai; 2010.

Kim D, Uk CJ, Cong PT, Wook JJ. Real-time object boundary tracing circuit based on FPGA. In: International conference on control, automation and systems, Seoul. IEEE; 2007. pp. 2307–2312.

Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006;27:861–874. doi: 10.1016/j.patrec.2005.10.010. DOI

Byrd KA, Zeng J, Chouikha M. A validation model for segmentation algorithms of digital mammography images. J Appl Sci Eng Technol. 2007;1:41–50.

Prochazka A, Vysata O, Tupa O, Yadollahi M, Valis M. Discrimination of axonal neuropathy using sensitivity and specificity statistical measures. Neural Comput Appl. 2014;25(6):1349–1358. doi: 10.1007/s00521-014-1622-0. DOI

Shattuck DW, Prasad G, Mirza M, Narr KL, Toga AW. Online resource for validation of brain segmentation methods. NeuroImage. 2009;45(2):431–439. doi: 10.1016/j.neuroimage.2008.10.066. PubMed DOI PMC

Chao A, Chazdon RL, Colwell RK, Shen TJ. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol Lett. 2005;8:148–159. doi: 10.1111/j.1461-0248.2004.00707.x. DOI

Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells WM, Jolesz FA, Kikinis R. Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol. 2004;11(2):178–189. doi: 10.1016/S1076-6332(03)00671-8. PubMed DOI PMC