Abstract
Dental biometrics is a type of biometrics that uses dental information to identify individuals. Well-known biometric features such as fingerprints and gait images have been successfully used to identify individuals. However, these features can be easily damaged. Teeth are more durable than other biometric features. Therefore, dental biometrics are used when other biometric features are not available. There are several types of dental radiographs. Panoramic radiographs are a type of x-ray that show the entire jaw. In these x-rays, all the teeth are viewed together. Panoramic x-rays contain more information about the tooth and jaw structures. However, they also contain unwanted elements such as the bite disc, mandible, nasal bone, etc. This makes them more difficult to process. All types of dental radiographs have difficulties in processing due to slight differences in brightness, overlapping or differently aligned teeth. Identifying individuals from dental radiographs often involves the following main steps: jaw separation, tooth segmentation, feature extraction, and feature matching. The accuracy of jaw and tooth segmentation influences the next steps. In this study, a new fully automatic method for separating mandible, maxilla, and teeth in panoramic radiographs is proposed. The proposed method achieved high accuracy in jaw separation. It also achieved better jaw separation performance than comparable studies. Although the proposed method is a fully automatic method, its performance in tooth separation is close to that of the compared semi-automatic method. In the proposed study, a jaw separation ratio of 0.99, based on the number of correctly aligned teeth, was achieved. The detection rate of the separators for the teeth in the mandibular jaw is 0.90 and the accuracy is 0.86. For maxillary teeth, these values are 0.92 and 0.90, respectively. The results are promising for the automatic segmentation of panoramic radiographs for human identification.
Similar content being viewed by others
Data availability
Raw data were generated at Intelligent Vision Research Lab. Derived data supporting the findings of this study are available from the authors of [36] on request.
References
Abdel-Mottaleb, M, Nomir, O, Nassar, DE, Fahmy, G, Ammar, HH (2003) Challenges of developing an automated dental identification system. 2003 46th Midwest symposium on circuits and systems, 1, 411–414. https://doi.org/10.1109/MWSCAS.2003.1562306
Abdi H, Kasaei S, Mehdizadeh M (2015) Automatic segmentation of mandible in panoramic x-ray. J Med Imaging 2(4):44003
Ajaz, A, Kathirvelu, D (2013) Dental biometrics: computer aided human identification system using the dental panoramic radiographs. 2013 international conference on communication and signal processing, 717–721. https://doi.org/10.1109/iccsp.2013.6577149
Al-Sherif, N, Guo, G, Ammar, HH (2012) A new approach to teeth segmentation. Proceedings - 2012 IEEE international symposium on multimedia, ISM 2012, (09), 145–148. https://doi.org/10.1109/ISM.2012.35
Avuçlu E, Başçiftçi F (2019) Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques. Chaos, Solitons Fractals 120:127–138. https://doi.org/10.1016/j.chaos.2019.01.023
Banday M, Mir AH (2018) Forensic dental biometry-a human identification system using panoramic dental radiographs based on shape of mandibular bone. Int J Biom 10(4):291–314. https://doi.org/10.1504/IJBM.2018.095284
Bozkurt MH, Karagol S (2020) Jaw and teeth segmentation on the panoramic X-ray images for dental human identification. J Digit Imaging 33(6):1410–1427. https://doi.org/10.1007/s10278-020-00380-8
Cai W, Liu D, Ning X, Wang C, Xie G (2021) Voxel-based three-view hybrid parallel network for 3D object classification. Displays 69:102076
Dibeh, G, Hilal, A, Charara, J (2018) A Novel Approach for Dental Panoramic Radiograph Segmentation. 2018 IEEE international multidisciplinary conference on engineering technology, IMCET 2018, 1–6. https://doi.org/10.1109/IMCET.2018.8603043
Frejlichowski, D, Wanat, R (2011) Automatic segmentation of digital Orthopantomograms for forensic human identification. In lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): Vol. 6979 LNCS (pp. 294–302). https://doi.org/10.1007/978-3-642-24088-1_31
Ghodsi, SB, Faez, K (2012) A novel approach for matching of dental radiograph image using Zernike moment. CSAE 2012 - proceedings, 2012 IEEE international conference on computer science and automation engineering, 3, 303–306. https://doi.org/10.1109/CSAE.2012.6272960
Hofer, M, Marana, AN (2007) Dental biometrics: human identification based on dental work information. XX Brazilian symposium on computer graphics and image processing (SIBGRAPI 2007), (dc), 281–286. https://doi.org/10.1109/SIBGRAPI.2007.9
Jaffino G, Banumathi A, Gurunathan U, Vijayakumari B, Jose JP (2017) A new mathematical modelling based shape extraction technique for forensic odontology. J Forensic Legal Med 47(June):39–45. https://doi.org/10.1016/j.jflm.2017.02.006
Jain AK, Chen H (2004) Matching of dental X-ray images for human identification. Pattern Recognit 37(7):1519–1532 Ffffffgk pl 0mmmmmmmmbcvb
Jain, AK, Chen, H, Minut, S (2003) Dental biometrics: human identification using dental radiographs. In lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 2688, pp. 429–437). https://doi.org/10.1007/3-540-44887-X_51
Jain, AK, Flynn, P, Ross, A (2007) Handbook of biometrics. Springer Science & Business Media
Lira PH, Giraldi GA, Neves LA (2009) Panoramic dental X-Ray image segmentation and feature extraction. In: Proceedings of V workshop of computing vision, Sao Paulo, Brazil
López VRF, Adorno CG, Román JCM, Noguera JLV, Silva RG, Legal-Ayala H, Mello-Román JD, Torres RDE, Facon J (2021) Panoramic radiography database (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4457648
Maurya M, Narvekar S, Naik S, Shet S, Borkar S (2013) Computerized approach for dental identification using radiographs. Int J Sci Res Publ 3(1):2250–3153 Retrieved from www.ijsrp.org
Nassar, DEM, Chaudhry, FU, Ammar, HH (2004) On performance evaluation of image segmentation algorithms: success is not all or none. Proc 1st Int Comput Eng Conf, 354–359
Nomir O, Abdel-Mottaleb M (2005) A system for human identification from X-ray dental radiographs. Pattern Recogn 38(8):1295–1305. https://doi.org/10.1016/j.patcog.2004.12.010
Nomir, O, Abdel-Mottaleb, M (2006) Hierarchical dental x-ray radiographs matching. Proceedings - international conference on image processing, ICIP, (2001), 2677–2680. https://doi.org/10.1109/ICIP.2006.313061
Oktay AB (2018) Human identification with dental panoramic radiographic images. IET Biom 7(4):349–355. https://doi.org/10.1049/iet-bmt.2017.0078
Ølberg J, Goodwin M (2016) Automated dental identification with lowest cost path-based teeth and jaw separation. Scand J Forensic Sci 22(2):44–56. https://doi.org/10.1515/sjfs-2016-0008
Permata, NA, Setiawardhana, Sigit, R (2017) Forensic identification system using dental panoramic radiograph. Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017, 2017-Janua, 281–287. https://doi.org/10.1109/KCIC.2017.8228600
Pushparaj V, Gurunathan U, Arumugam B (2013) An effective dental shape extraction algorithm using contour information and matching by mahalanobis distance. J Digit Imaging 26(2):259–268. https://doi.org/10.1007/s10278-012-9492-4
Rabbani, GS, Sultana, S, Hasan, MN, Fahad, SQ, Uddin, J (2019) Person identification using SURF features of dental radiograph. Proceedings of the 3rd international conference on cryptography, security and privacy - ICCSP '19, 196–200. https://doi.org/10.1145/3309074.3309115
Rad AE, Rahim MSM, Kolivand H, Norouzi A (2018) Automatic computer-aided caries detection from dental x-ray images using intelligent level set. Multimed Tools Appl 77(21):28843–28862. https://doi.org/10.1007/s11042-018-6035-0
Raja R, Sinha TS, Dubey RP (2015) Recognition of human-face from side-view using progressive switching pattern and soft-computing technique. Association for the advancement of modelling and simulation techniques in enterprises. Advance B 58(1):14–34
Raja R, Sinha TS, Patra RK, Tiwari S (2018) Physiological trait-based biometrical authentication of human-face using LGXP and ANN techniques. Int J Inf Comput Secur 10(2–3):303–320
Rani S, Rajani N, Reddy S (2012) Comparative study on content based image retrieval. Int J Future Comput Commun 1(4):366–368
Rehman, F, Akram, MU, Faraz, K, Riaz, N (2015) Human identification using dental biometric analysis. 2015 5th international conference on digital information and communication technology and its applications, DICTAP 2015, 96–100. https://doi.org/10.1109/DICTAP.2015.7113178
Román JCM, Fretes VR, Adorno CG, Silva RG, Noguera JLV, Legal-Ayala H, Mello-Román JD, Torres RDE, Facon J (2021) Panoramic dental radiography image enhancement using multiscale mathematical morphology. Sensors 21:3110. https://doi.org/10.3390/s21093110
Said EH, Nassar DEM, Fahmy G, Ammar HH (2006) Teeth segmentation in digitized dental x-ray films using mathematical morphology. IEEE Trans Inf Forensics Secur 1(2):178–189. https://doi.org/10.1109/TIFS.2006.873606
Shamsafar, F (2013) A new feature extraction method from dental X-ray images for human identification. Iranian Conference on Machine Vision and Image Processing, MVIP, 397–402. https://doi.org/10.1109/IranianMVIP.2013.6780018
Silva G, Oliveira L, Pithon M (2018) Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Exp Syst Appl 107:15–31. https://doi.org/10.1016/j.eswa.2018.04.001
Srivastava, A, Aggarwal, AK (2018) Medical image fusion in spatial and transform domain: A comparative analysis. In handbook of research on advanced concepts in real-time image and video processing (pp. 281-300). IGI global
Srivastava, A, Singhal, V, Aggarawal, AK (2017) Comparative analysis of multimodal medical image fusion using PCA and wavelet transforms. Int J Latest Technol Eng Manag Appl Sci(IJLTEMAS) VI
Tiwari L, Raja R, Awasthi V, Miri R, Sinha GR, Alkinani MH, Polat K (2021) Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms. Measurement 172:108882
You H, Yu L, Tian S, Ma X, Xing Y, Xin N, Cai W (2021) MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network. Knowl Based Syst 231:107456
Zhou J, Abdel-Mottaleb M (2005) A content-based system for human identification based on bitewing dental X-ray images. Pattern Recogn 38(11):2132–2142. https://doi.org/10.1016/j.patcog.2005.01.011
Funding
No funds, grants, or other support was received. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bozkurt, M.H., Karagol, S. Statistical elimination based approach to jaw and tooth separation on panoramic radiographs for dental human identification. Multimed Tools Appl 82, 32117–32150 (2023). https://doi.org/10.1007/s11042-023-14746-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14746-x