Farklı Klinik Işıklarında Metamerizmi Önlemek İçin Renk Anları Kullanılarak Makine Öğrenmesi Tabanlı Diş Rengi Değerlendirmesi

Abstract

Protetik diş tedavisinde doğru renk seçimi, hem doğal bir estetik görünüm elde edilmesi hem de hastanın tedaviye olan memnuniyetinin artırılması açısından büyük önem taşımaktadır. Ancak, bu süreç pek çok teknik ve çevresel faktörden etkilenmektedir. Özellikle klinik ve laboratuvar ortamlarındaki değişen ışık kaynakları, renk algısında yanıltıcı sonuçlara yol açan metamerizm sorununa neden olmaktadır. Bu çalışma, farklı ışık kaynaklarında renk tespiti yaparak metamerizmin etkisini azaltan, geleneksel renk eşleştirme yöntemlerinin subjektifliğini ortadan kaldıran ve maliyetli ölçüm cihazlarına alternatif sunan bir yöntem önermektedir. Vita 3D Master renk skalasında bulunan 29 renk örneği, dört farklı klinik ışık kaynağında beşer kez görüntülenmiştir. RGB, LAB ve HSV renk uzaylarında renk anları kullanılarak öznitelik çıkarımı yapılmıştır. Bu verilerle oluşturulan veri setleri üzerinde farklı makine öğrenmesi algoritmaları ile deneysel çalışmalar gerçekleştirilmiştir. Sonuçta, dört klinik ışık koşulunun sınıflandırılmasında %100, 29 Vita renginin ışıktan bağımsız sınıflandırılmasında %85, beyaz ışık altında %100, doğal ışık altında %97, flaş ışığında %92 ve sarı ışık altında %94 doğruluk oranları elde edilmiştir. Bu bulgular, geleneksel veya maliyetli renk seçim süreçlerinin sınırlamalarının aşılabileceğini ve metamerizmin makine öğrenmesi teknikleriyle azaltılabileceğini göstermiştir.

Keywords

• Renk anları
• Makine öğrenmesi
• Vita renk uyumu
• Metamerizm
• Protez diş tedavisi

Project Number

Bu çalışma, 123E597 proje kodu kapsamında Türkiye Bilimsel ve Teknik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir.

Machine Learning-Based Tooth Color Assessment Using Color Moments to Prevent Metamerism in Different Clinical Lights

Abstract

Choosing the right shade in prosthodontic treatment is of great importance in terms of achieving a natural aesthetic appearance and increasing the patient's satisfaction with the treatment. However, this process is affected by many technical and environmental factors. In particular, variable light sources in clinical and laboratory environments cause the problem of metamerism, which leads to misleading results in color perception. This study proposes a method that reduces the effect of metamerism by detecting color under different light conditions, eliminates the subjectivity of traditional color matching methods and offers an alternative to costly measurement devices. The 29 color samples from the Vita 3D Master shade guide were imaged five times each in four different clinical light conditions. Feature extraction was performed using color moments in RGB, LAB and HSV color spaces. Experimental studies were carried out with different machine learning algorithms on the datasets created with these data. As a result, 100% accuracy was obtained for the classification of four clinical light conditions, 85% for the light-independent classification of 29 Vita colors, 100% under white light, 97% under natural light, 92% under flash light and 94% under yellow light. These findings demonstrated that the limitations of traditional or costly color selection processes can be overcome and metamerism can be reduced by machine learning techniques.

Keywords

• Color Moments
• Machine Learning
• Vita Shade Matching
• Metamerism
• Prosthetic Tooth
• Treatment

Project Number

Bu çalışma, 123E597 proje kodu kapsamında Türkiye Bilimsel ve Teknik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmektedir.

References

1. Abraham, G., Kurian, N., Wadhwa, S., & Varghese, K. G. (2023). A smartphone application with a gray card for clinical shade selection: A technique. The Journal of Prosthetic Dentistry. https://doi.org/10.1016/j.prosdent.2023.01.016
2. Basavanna, R. S., Gohil, C., & Shivanna, V. (2013). Shade selection. International Journal of Oral Health Sciences, 3(1), 26-31. https://doi.org/10.4103/2231-6027.122097
3. Bernauer, S. A., Zitzmann, N. U., & Joda, T. (2021). The use and performance of artificial intelligence in prosthodontics: A systematic review. Sensors, 21(19), 6628. https://doi.org/10.3390/s21196628
4. Borse, S., & Chaware, S. H. (2020). Tooth shade analysis and selection in prosthodontics: A systematic review and meta-analysis. The Journal of Indian Prosthodontic Society, 20(2), 131-140. https://doi.org/10.4103/jips.jips_399_19
5. Fayed, A. E. M., Mohamed, H. A., & Othman, H. I. (2022). A Comparison between visual shade matching and digital shade analysis system using K-NN algorithm. Al-Azhar Journal of Dental Science, 25(2), 133-141. https://doi.org/10.21608/ajdsm.2021.85035.1211
6. Grischke, J., Johannsmeier, L., Eich, L., Griga, L., & Haddadin, S. (2020). Dentronics: Towards robotics and artificial intelligence in dentistry. Dental Materials, 36(6), 765-778. https://doi.org/10.1016/j.dental.2020.03.021
7. Hu, J. C., Wang, C. H., & Kuhns, D. (2016). New Algorithm in Shade Matching. Journal of Cosmetic Dentistry, 32(1).
8. Jarad, F. D., Russell, M. D., & Moss, B. W. (2005). The use of digital imaging for colour matching and communication in restorative dentistry. British Dental Journal, 199(1), 43-49. https://doi.org/10.1038/sj.bdj.4812559

9. Justiawan, Wahjuningrum, D. A., Hadi, R. P., Nurhayati, A. P., Prayogo, K., Sigit, R., & Arief, Z. (2019). Comparative analysis of color matching system for teeth recognition using color moment. Medical Devices: Evidence and Research, 497-504. https://doi.org/10.2147/MDER.S224280
10. Khanagar, S. B., Al-Ehaideb, A., Maganur, P. C., Vishwanathaiah, S., Patil, S., Baeshen, H. A., ... & Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry–A systematic review. Journal of Dental Sciences, 16(1), 508-522. https://doi.org/10.1016/j.jds.2020.06.019
11. Kim, M., Kim, B., Park, B., Lee, M., Won, Y., Kim, C. Y., & Lee, S. (2018). A digital shade-matching device for dental color determination using the support vector machine algorithm. Sensors, 18(9), 3051. https://doi.org/10.3390/s18093051
12. Kim-Pusateri, S., Brewer, J. D., Davis, E. L., & Wee, A. G. (2009). Reliability and accuracy of four dental shade-matching devices. The Journal of Prosthetic Dentistry, 101(3), 193-199. https://doi.org/10.1016/S0022-3913(09)60028-7
13. Koklu, M., Kursun, R., Taspinar, Y. S., & Cinar, I. (2021). Classification of date fruits into genetic varieties using image analysis. Mathematical Problems in Engineering, 2021, 1-13. https://doi.org/10.1155/2021/4793293
14. Liberato, W. F., Barreto, I. C., Costa, P. P., de Almeida, C. C., Pimentel, W., & Tiossi, R. (2019). A comparison between visual, intraoral scanner, and spectrophotometer shade matching: A clinical study. The Journal of Prosthetic Dentistry, 121(2), 271-275. https://doi.org/10.1016/j.prosdent.2018.05.004
15. Lin, T. L., Chuang, C. H., Chen, S. L., Lin, N. H., Miaou, S. G., Lin, S. Y., ... & Villaverde, J. F. (2019). An efficient image processing methodology based on fuzzy decision for dental shade matching. Journal of Intelligent & Fuzzy Systems, 36(2), 1133-1142. https://doi.org/10.3233/JIFS-169887
16. Liu, J., Zhao, N., & He, R. (2013, October). Study of color matching system for porcelain teeth. In 2013 IEEE International Conference on Medical Imaging Physics and Engineering (pp. 310-314). https://doi.org/10.1109/ICMIPE.2013.6864558
17. Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386. https://doi.org/10.21275/ART20203995
18. Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3523-3542. https://doi.org/10.1109/TPAMI.2021.3059968
19. Mohammadi, A., Bakhtiari, Z., Mighani, F., & Bakhtiari, F. (2021). Validity and reliability of tooth shade selection by smartphone photography and software applications. The Journal of the Indian Prosthodontic Society, 21(3), 281. https://doi.org/10.4103/jips.jips_193_21
20. Mutlag, W. K., Ali, S. K., Aydam, Z. M., & Taher, B. H. (2020, July). Feature extraction methods: A review. In Journal of Physics: Conference Series (Vol. 1591, No. 1, p. 012028). IOP Publishing. https://doi.org/10.1088/1742-6596/1591/1/012028
21. Oh, W. S., Pogoncheff, J., & O’Brien, W. J. (2010). Digital computer matching of tooth shade. Materials, 3(6), 3694-3699. https://doi.org/10.3390/ma3063694
22. Öngül, D., Şermet, B., & Balkaya, M. C. (2012). Visual and instrumental evaluation of color match ability of 2 shade guides on a ceramic system. The Journal of Prosthetic Dentistry, 108(1), 9-14. https://doi.org/10.1016/S0022-3913(12)60102-4.
23. Paravina, R. D., Westland, S., Imai, F. H., Kimura, M., & Powers, J. M. (2006). Evaluation of blending effect of composites related to restoration size. Dental Materials, 22(4), 299-307. https://doi.org/10.1016/j.dental.2005.04.022
24. Sampaio, C. S., Gurrea, J., Gurrea, M., Bruguera, A., Atria, P. J., Janal, M., ... & Hirata, R. (2018). Dental shade guide variability for hues B, C, and D using cross-polarized photography. International Journal of Periodontics & Restorative Dentistry. https://doi.org/10.11607/prd.3270
25. Seghi, R. R., Hewlett, E. R., & Kim, J. (1989). Visual and instrumental colorimetric assessments of small color differences on translucent dental porcelain. Journal of dental research, 68(12), 1760-1764. https://doi.org/10.1177/00220345890680120801
26. Shammas, M., & Alla, R. K. (2011). Color and shade matching in dentistry. Trends Biomater Artif Organs, 25(4), 172-5.
27. Sigit, R., & Arief, Z. (2017). Tooth shade assessment using PCA and KNN classifier algorithm based on color moment. EMITTER International Journal of Engineering Technology, 5(1), 139-153. https://doi.org/10.24003/emitter.v5i1.171
28. Tam, W. K., & Lee, H. J. (2012). Dental shade matching using a digital camera. Journal of dentistry, 40, e3-e10. https://doi.org/10.1016/j.jdent.2012.06.004
29. Tam, W. K., & Lee, H. J. (2017). Accurate shade image matching by using a smartphone camera. Journal of Prosthodontic Research, 61(2), 168-176. https://doi.org/10.1016/j.jpor.2016.07.004
30. Ueki, K., Wakamatsu, H., & Hagiwara, Y. (2020, October). Evaluation of dental prosthesis colors using a neural network. In 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP) (pp. 210-214). IEEE. https://doi.org/10.1109/ICSIP49896.2020.9339381
31. Villarroel, M., Fahl, N., De Sousa, A. M., & de Oliveira, O. B. (2011). Direct esthetic restorations based on translucency and opacity of composite resins. Journal of Esthetic and Restorative Dentistry, 23(2), 73-87. https://doi.org/10.1111/j.1708-8240.2010.00392.x
32. Wang, J., Lin, J., Gil, M., Seliger, A., Da Silva, J. D., & Ishikawa-Nagai, S. (2014). Assessing the accuracy of computer color matching with a new dental porcelain shade system. The Journal of prosthetic dentistry, 111(3), 247-253. https://doi.org/10.1016/j.prosdent.2013.07.008
33. Wanna, Y., Wiratchawa, K., Leenaracharoongruang, R., Sittiwong, W., Panpisut, P., & Intharah, T. (2022, July). DentShadeAI: a Framework for Automatic Dental Shade Matching through Mobile Phone Camera. In 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) (pp. 282-285). IEEE. https://doi.org/10.1109/ITC-CSCC55581.2022.9894968
34. Westland, S., Luo, W., Ellwood, R., Brunton, P., & Pretty, I. (2007). Colour assessment in dentistry. Annals of the BMVA, 2007(4), 1-10.