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Caries Detection in Non-standardized Periapical Dental X-Rays

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Computer Aided Intervention and Diagnostics in Clinical and Medical Images

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 31))

Abstract

Dental caries are currently one of the most prevalent diseases in the modern world. Early detection and diagnosis of the disease is the best treatment available to dental healthcare professionals and is crucial in preventing advanced stages of decay. This paper presents an effective model for caries detection across a variety of non-uniform X-rays using individual tooth segmentation, boundary detection and caries detection through image analysis techniques. The tooth segmentation is implemented using integral projection and an analytical division algorithm. The boundary detection is implemented through the use of top and bottom hat transformations and active contours. Finally, the caries detection was achieved through the use of blob detection and cluster analysis on suspected carious regions. The cluster analysis generates its results relative to the image being analyzed and as such, forms the unsupervised evaluation approach of this paper. The viability of this unsupervised learning model, and its relative effectiveness of accurately diagnosing dental caries when compared to current systems is indicated by the results detailed in this paper, with the proposed model achieving a 96% correct diagnostic.

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References

  1. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, Shibuya K, Salomon JA, Abdalla S, Aboyans V et al (2013) Years lived with disability (ylds) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the global burden of disease study 2010. The Lancet 380(9859):2163–2196

    Article  Google Scholar 

  2. Booshehry MZ, Fasihinia H, Khalesi M, Gholami L (2011) Dental caries diagnostic methods. DJH 2(1)

    Google Scholar 

  3. Amaechi BT (2009) Emerging technologies for diagnosis of dental caries: the road so far. J Appl Phys 105(10):102047

    Article  Google Scholar 

  4. Noor NM, Khalid NEA, Ali MH, Numpang ADA (2010) Fish bone impaction using adaptive histogram equalization (ahe). In: (2010) Second international conference on computer research and development. IEEE, pp 163–167

    Google Scholar 

  5. Sakata M, Ogawa K (2009) Noise reduction and contrast enhancement for small-dose x-ray images in wavelet domain. In: (2009) IEEE nuclear science symposium conference record (NSS/MIC). IEEE, pp 2924–2929

    Google Scholar 

  6. Ahmad SA, Taib MN, Khalid NEA, Taib H (2012) An analysis of image enhancement techniques for dental x-ray image interpretation. Int J Mach Learn Comput 2(3):292–297

    Article  Google Scholar 

  7. Bharathi KK, Muruganand S, Periasamy A (2014) Digital image processing based noise reduction analysis of digital dental xray image using matlab. J NanoSci NanoTechnol 2(1):198–203

    Google Scholar 

  8. Nomir O, Abdel-Mottaleb M (2005) A system for human identification from x-ray dental radiographs. Pattern Recognit 38(8):1295–1305

    Article  Google Scholar 

  9. Hu S, Hoffman EA, Reinhardt JM (2001) Automatic lung segmentation for accurate quantitation of volumetric x-ray ct images. IEEE Trans Med Imaging 20(6):490–498

    Article  Google Scholar 

  10. Nomir O, Abdel-Mottaleb M (2007) Human identification from dental x-ray images based on the shape and appearance of the teeth. IEEE Trans Inf Forens Sec 2(2):188–197

    Article  Google Scholar 

  11. Lin P-L, Lai Y-H, Huang P-W (2012) Dental biometrics: human identification based on teeth and dental works in bitewing radiographs. Pattern Recognit 45(3):934–946

    Article  Google Scholar 

  12. Jain AK, Chen H (2004) Matching of dental x-ray images for human identification. Pattern Recognit 37(7):1519–1532

    Article  Google Scholar 

  13. Frejlichowski D, Wanat R (2011) Automatic segmentation of digital orthopantomograms for forensic human identification. In: International conference on image analysis and processing. Springer, pp 294–302

    Google Scholar 

  14. Zhou J, Abdel-Mottaleb M (2005) A content-based system for human identification based on bitewing dental x-ray images. Pattern Recognit 38(11):2132–2142

    Article  Google Scholar 

  15. Oliveira J (2009) Caries detection in panoramic dental X-ray images

    Google Scholar 

  16. Rad AE, Mohd MS Rahim, Rehman A, Altameem A, Saba T (2013) Evaluation of current dental radiographs segmentation approaches in computer-aided applications. IETE Tech Rev 30(3):210–222

    Article  Google Scholar 

  17. Solanki A, Jain K, Desai N (2013) Isef based identification of rct/filling in dental caries of decayed tooth. Int J Image Process (IJIP) 7(2):149–162

    Google Scholar 

  18. Oprea S, Marinescu C, Lita I, Jurianu M, Visan DA, Cioc IB (2008) Image processing techniques used for dental x-ray image analysis. In: (2008) 31st international spring seminar on electronics technology. IEEE, pp 125–129

    Google Scholar 

  19. Zhang H, Boyles MJ, Ruan G, Li H, Shen H, Ando M (2013) Xsede-enabled high-throughput lesion activity assessment. In: Proceedings of the conference on extreme science and engineering discovery environment: gateway to discovery. ACM, p 10

    Google Scholar 

  20. Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):79–116

    Article  Google Scholar 

  21. Chu SJ (2007) Range and mean distribution frequency of individual tooth width of the maxillary anterior dentition. Pract Proc Aesthet Dent 19(4):209

    Google Scholar 

  22. 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

    Article  Google Scholar 

  23. Shah S, Abaza A, Ross A, Ammar H (2006) Automatic tooth segmentation using active contour without edges. In: (2006) Biometrics symposium: special session on research at the biometric consortium conference. IEEE, pp 1–6

    Google Scholar 

  24. Lai Y, Lin P (2008) Effective segmentation for dental x-ray images using texture-based fuzzy inference system. In: International conference on advanced concepts for intelligent vision systems. Springer, pp 936–947

    Google Scholar 

  25. Tracy KD, Dykstra BA, Gakenheimer DC, Scheetz JP, Lacina S, Scarfe WC, Farman AG (2010) Utility and effectiveness of computer-aided diagnosis of dental caries. Gen Dent 59(2):136–144

    Google Scholar 

  26. Dykstra B (2008) Interproximal caries detection: how good are we? Dent Today 27(4):144–146

    Google Scholar 

  27. Valizadeh S, Goodini M, Ehsani S, Mohseni H, Azimi F, Bakhshandeh H (2015) Designing of a computer software for detection of approximal caries in posterior teeth. Iranian J Radiol 12(4)

    Google Scholar 

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    D. Osterloh & Serestina Viriri

Authors
  1. D. Osterloh
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  2. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    J. Dinesh Peter

  2. Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, USA

    Steven Lawrence Fernandes

  3. Brazilian Computer Society, Aalcides Platiny Alves Batista , Porto Alegre, Rio Grande do Sul, Brazil

    Carlos Eduardo Thomaz

  4. School of Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Serestina Viriri

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Osterloh, D., Viriri, S. (2019). Caries Detection in Non-standardized Periapical Dental X-Rays. In: Peter, J., Fernandes, S., Eduardo Thomaz, C., Viriri, S. (eds) Computer Aided Intervention and Diagnostics in Clinical and Medical Images. Lecture Notes in Computational Vision and Biomechanics, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-04061-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-04061-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04060-4

  • Online ISBN: 978-3-030-04061-1

  • eBook Packages: EngineeringEngineering (R0)

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