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Domain Study and Literature Review

  • Kavindra R. Jain
  • N. C. Chauhan
Chapter

Abstract

Many researchers make use of thresholding and morphological operations for feature extraction and segmentation. Efficient operations are still not included in the existing dental software majorly used by dental practitioners. Hence, the effective benefits of these methods are still not available to the end users. Some of the research work has been reported for human identification, but very few researchers have applied and realised the methods for diagnosis purpose. Geometrical features for measurements like area, length and angle are not detected by all software even though they are considered the basic features especially for the diagnosis of intra-oral diseases. Interactive portions of X-ray selected for further processing specifically for the purpose of diagnosis is the need of the hour as it would help both doctors and patients to understand the problem and depth of disease. No software exploits the power of AI tools and techniques such as neural network and fuzzy C-means. The usage of such methods may help better in identification and diagnosis of dental cavities. Exploration, development, and use of different automated and semi-automated methods for the analysis of dental radiographs may lead to progress in the knowledge and usage of more such methods that can be used for identification and diagnosis of some dental diseases. The overall contribution of this thesis attempts to make progress on these objectives which may finally contribute as an add-on help to dental practitioners and patients at large.

Keywords

Dental radiographs Medical image processing Segmentation Morphological operation Fuzzy C-means KFCM Intra-oral diseases 

Bibliography

  1. 10.
    Sakuma, A. (2012). Three-dimensional visualization of composite fillings for dental identification using CT images. Dentomaxillofacial Radiology, 41(6), 515–519.CrossRefGoogle Scholar
  2. 11.
    Kamburoǧlu, K., Kolsuz, E., Murat, S., Yüksel, S., & Özen, T. (2012). Proximal caries detection accuracy using intraoral bitewing radiography, extraoral bitewing radiography and panoramic radiography. Dentomaxillofacial Radiology, 41(6), 450–459.CrossRefGoogle Scholar
  3. 12.
    Shafer, W., & Levy, B. M. (1983). A textbook of oral pathology. Philadelphia, PA: Saunders.Google Scholar
  4. 13.
    Ahmad, S. A., Taib, M. N., Khalid, N. E. A., Ahmad, R., & Taib, H. (2011). Performance of compound enhancement algorithms on dental radiograph images. World Academy of Science, Engineering and Technology, 50, 658–663.Google Scholar
  5. 14.
    Michel, S., Kolller, S. M., Ruh, M., & Schwaninger, A. (2007). Do ‘image enhancement’ functions really enhance X-ray image interpretation? In Proceedings of the 29th annual cognitive science society, pp. 1301–1306.Google Scholar
  6. 15.
    Said, E. H., Nassar, D. E. M., Fahmy, G., & Ammar, H. H. (2006). Teeth segmentation in digitized dental x-ray films using mathematical morphology. IEEE Transactions on Information Forensics and Security, 1(2), 178–189.CrossRefGoogle Scholar
  7. 16.
    Dighe, S. C., & Shriram, R. (2012). Dental biometrics for human identification based on dental work and image properties in Periapical radiographs. In IEEE region 10 annual international conference, proceedings/TENCON.Google Scholar
  8. 17.
    Kiattisin, S., Leelasantitham, A., Chamnongthai, K., & Higuchi, K. (2008). A match of X-ray teeth films using image processing based on special features of teeth. In Proceedings of the SICE annual conference, pp. 35–39.Google Scholar
  9. 18.
    Prajapati, D. B., Desai, N. P., & Modi, C. K. (2012). A simple and novel CBIR technique for features extraction using AM dental radiographs. In Proceedings – International conference on communication systems and network technologies, CSNT 2012, pp. 198–202.Google Scholar
  10. 19.
    Omanovic, M., & Orchard, J. J. (2008). Image registration-based approach to ranking dental x-ray images for human forensic identification. Canadian Society of Forensic Science Journal, 41(3), 125–134.CrossRefGoogle Scholar
  11. 2.
    Yousefi, B., Hakim, H., Motahir, N., Yousefi, P., & Hosseini, M. M. (2012). Visibility enhancement of digital dental X-ray for RCT application using Bayesian classifier and two times wavelet image fusion. Journal of American Science, 8(1), 7–13.Google Scholar
  12. 20.
    Abdullah, S. L. S., Hambali, H., & Jamil, N. (2012). Segmentation of natural images using an improved thresholding-based technique. Procedia Engineering, 41(Iris), 938–944.CrossRefGoogle Scholar
  13. 21.
    Nomir, O., & Abdel-Mottaleb, M. (2005). A system for human identification from X-ray dental radiographs. Pattern Recognition, 38(8), 1295–1305.CrossRefGoogle Scholar
  14. 22.
    Tiwari, R. B., Sant, S., Baba, G., & Yardi, P. A. R. (2006). Dental X-ray image enhancement based on human visual system and local image statistics. In Proceeding of the international conference of image processing, Computer Vision and Pattern Recognition, pp. 100–106.Google Scholar
  15. 23.
    Zhai, X. M., Lu, H. D., & Zhang, L. Z. (2009). Application of image segmentation technique in tongue diagnosis. In Proceedings – 2009 international forum on information technology and applications, IFITA 2009, vol. 2, pp. 768–771.Google Scholar
  16. 24.
    Zhong, X., Fu, H., Yang, J., & Wang, W. (2009). Automatic segmentation in tongue image by mouth location and active appearance model. In 8th IEEE international symposium on dependable, autonomic and secure computing, DASC 2009, pp. 413–417.Google Scholar
  17. 25.
    Li, W., Hu, S., Wang, S., & Xu, S. (2009). Towards the objectification of tongue diagnosis: Automatic segmentation of tongue image. In IECON proceedings (industrial electronics conference), pp. 2121–2124.Google Scholar
  18. 26.
    Wang, Y. G., Yang, J., Zhou, Y., & Wang, Y. Z. (2007). Region partition and feature matching based color recognition of tongue image. Pattern Recognition Letters, 28(1), 11–19.CrossRefGoogle Scholar
  19. 27.
    Abesi, F. (2012). Diagnostic accuracy of digital and conventional radiography in the detection of non-cavitated approximal dental caries. Iranian Journal of Radiology, 9(1), 249–254.CrossRefGoogle Scholar
  20. 28.
    Ehsani Rad, A., Mohd Rahim, M. S., & Norouzi, A. (2013). Digital dental X-ray image segmentation and feature extraction. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11(6), 3109–3114.CrossRefGoogle Scholar
  21. 29.
    Huang, P. W., Lin, P. L., Kuo, C. H., & Cho, Y. S. (2012). An effective tooth isolation method for bitewing dental X-ray images. In Proceedings – International conference on machine learning and cybernetics, vol. 5, pp. 1814–1820.Google Scholar
  22. 3.
    White, S. C., & Pharoah, M. J. (2004). Oral radiology, principles and interpretation. St. Louis, MO: Mosby.Google Scholar
  23. 30.
    Nomir, O., & Abdel-Mottaleb, M. (2008). Hierarchical contour matching for dental X-ray radiographs. Pattern Recognition, 41(1), 130–138.CrossRefGoogle Scholar
  24. 31.
    Lira, P. H. M., Giraldi, G. A., & Neves, L. A. P. (2013) .Using the mathematical morphology and shape matching for automatic data extraction in dental X-ray images. In IX Workshop de Visão Computacional, p. 6.Google Scholar
  25. 32.
    Alsmadi, M. K. (2018). A hybrid fuzzy C-means and neutrosophic for jaw lesions segmentation. Ain Shams Engineering Journal, 9(4), 697–706.CrossRefGoogle Scholar
  26. 33.
    Tuan, T. M., Ngan, T. T., & Son, L. H. (2016). A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation. Applied Intelligence, 45(2), 402–428.CrossRefGoogle Scholar
  27. 34.
    Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.CrossRefGoogle Scholar
  28. 5.
    Oprea, S., Marinescu, C., Lita, I., Jurianu, M., Visan, D. A., & Cioc, I. B. (2008). Image processing techniques used for dental x-ray image analysis. In 2008 31st international spring seminar on electronics technology, pp. 125–129.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kavindra R. Jain
    • 1
  • N. C. Chauhan
    • 2
  1. 1.Department of Electronics and CommunicationG H Patel College of Engineering and TechnologyAnandIndia
  2. 2.Department of Information TechnologyA.D. Patel Institute of TechnologyAnandIndia

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