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An automatic and effective tooth isolation method for dental radiographs

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tooth isolation is a very important step for both computer-aided dental diagnosis and automatic dental identification systems, because it will directly affect the accuracy of feature extraction and, thereby, the final results of both types of systems. This paper presents an effective and fully automatic tooth isolation method for dental X-ray images, which contains up-per-lower jaw separation, single tooth isolation, over-segmentation verification, and under-segmentation detection. The upper-lower jaw separation mechanism is based on a gray-scale integral projection to avoid possible information loss and incorporates with the angle adjustment to handle skewed images. In a single tooth isolation, an adaptive windowing scheme for locating gap valleys is proposed to improve the accuracy. In over-segmentation, an isolation-curve verification scheme is proposed to remove excessive curves; and in under-segmentation, a missing-teeth detection scheme is proposed. The experimental results demonstrate that our method achieves the accuracy rates of 95.63% and 98.71% for the upper and lower jaw images, respectively, from the test database of 60 bitewing dental radiographs, and performs better for images with severe teeth occlusion, excessive dental works, and uneven illumination than that of Nomir and Abdel-Mottaleb's method. The method without upper-lower jaw separation step also works well for panoramic and periapical images.
Rocznik
Strony
126--136
Opis fizyczny
Bibliogr. 13 poz., rys., il., wykr.
Twórcy
autor
  • Department of Computer Science and Information Engineering, Providence University, Shalu, 200 Chung-chi Rd., Taichung, Taiwan 43301, R.O.C.
autor
  • Department of Computer Science and Engineering, National Chung-Hsing University, 250 Kuo-Kuang Rd., Taichung, Taiwan 402, R.O.C.
autor
  • Department of Computer Science and Engineering, National Chung-Hsing University, 250 Kuo-Kuang Rd., Taichung, Taiwan 402, R.O.C.
autor
  • Department of Computer Science and Engineering, National Chung-Hsing University, 250 Kuo-Kuang Rd., Taichung, Taiwan 402, R.O.C.
Bibliografia
  • 1. K. Jain, A. Ross, and S. Prabhakar, „An introduction to biometric recognition”, IEEE T. Circuits and Systems for Video Technology 14, 4–20 (2004).
  • 2. T. Schuller-Gotzburg and J. Suchanek, „Forensic odontologists successfully identify tsunami victims in Phuket”, Thailand Forensic Sci. Int. 171, 204–207 (2007).
  • 3. Y. H. Lai and P. L. Lin, „Effective segmentation for dental X-ray images using texture-based fuzzy inference system”, Proc. ACIVS Lect.Notes Comput. Sc. 5259, 936–947 (2008).
  • 4. P. L. Lin, Y. H. Lai, and P. W. Huang, „An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information”, Pattern Recogn. 4, 1380–1392 (2010).
  • 5. P.-L. Lin, Y.-H. Lai, and P.-W. Huang, „Dental biometrics: human identification based on teeth and dental works in bitewing radiographs”, Pattern Recogn. 3, 934–946 (2012).
  • 6. K. Jain and H. Chen, „Matching of dental X-ray images for human identification”, Pattern Recogn. 37, 1519–1532 (2004).
  • 7. O. Nomir and M. Abdel-Mottaleb, „A system for human identification from X-ray dental radiographs”, Pattern Recogn. 38, 1295–1305 (2005).
  • 8. J. Zhou and M. Abdel-Mottaleb, „A content-based system for human identification based on bitewing dental X-ray images,” Pattern Recogn. 38, 2132–2142 (2005).
  • 9. E. H. Said, D. E. M. Nassar, G. Fahmy, and H. H. Ammar, „Teeth segmentation in digitized dental x-ray films using mathematical morphology”, IEEE T. Inf. Foren. Sec. 1, 178–189 (2006).
  • 10. J. Oliveira and H Proenca, „Caries detection in panoramic dental X-ray images”, Computational Vision and Medical Image Processing, Computational Methods in Applied Science 19, 175–190 (2011).
  • 11. S. Li, T. Fevens, A. Krzyzak, C. Jin, and S. Li, „Semi-automatic computer aided lesion detection in dental X-rays using variational level set”, Pattern Recogn. 40, 2861–2873, (2007).
  • 12. L. Vincent, „Morphological grayscale reconstruction in image analysis: application and efficient algorithms”, IEEE T. Image Process. 2, 176–201 (1993).
  • 13. C. deBoor, „B(asic)-spline basics”, in Fundamental Developments of Computer-Aided Geometric Modelling, pp. 27–49, edited by L. Piegl, Academic Press, New York, 1993.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWAD-0033-0009
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