Segmentation of Dental Radiographs Using Active Contour Model

  • Kavindra R. Jain
  • N. C. Chauhan


The fundamental task in the case of medical images is detection of interested region(s) and their further investigations either in an automated fashion or through expert interventions. The presence of noise and image anomalies lead to imperfection in boundary detection as it depends on intensity and contrast. Basic image processing techniques/tools used priorly help the practioners for proper selction of region of interest. Sometimes it was observed during experimentation that regionof interest so selected by the edge detection algorithm in the tool are not clear and proper discretisation of the regions were not exact as desired.. So proper image segmentation for extraction of features is essential. To forfeit the above conditions partitioning of an image into subsections must satisfy certain conditions. Here, in the proposed snakes-based approach we took the points directly so that it can automatically extract the affected part. An entire interactive tool with GUI was developed in MATLAB and used to generate all results shown in this chapter. In addition to snakes model, some of the basic features like area and canny were also included in the GUI-based tool. This interactive user friendly approach can help practitioners in identification of region of interest which can be further used for a precancerous treatment. It was observed that cases with precancerous treatment under erythroplakia and leucoplakia were satisfactorily diagnosed and that results obtained were satisfactory for helping out the practitioners too.


Image segmentation Snakes model GUI Erythroplakia Leucoplakia Optimisation External energy Internal energy Active contour model 


  1. 44.
    Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer Vision, Graphics, and Image Processing, 29(1), 100–132.CrossRefGoogle Scholar
  2. 45.
    Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 898–916.CrossRefGoogle Scholar
  3. 46.
    Batchelor, B. G. (1994). Edge-region-based segmentation of range images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(3), 314–319.CrossRefGoogle Scholar
  4. 47.
    Kaganami, H. G., & Beiji, Z. (2009). Region-based segmentation versus edge detection. In IIH-MSP 2009 – 2009 5th international conference on intelligent information hiding and multimedia signal processing, pp. 1217–1221.Google Scholar
  5. 48.
    Muthukrishnan, R., & Radha, M. (2011). Edge detection techniques for image segmentation. International Journal of Computer Science, 3(6), 259–267.Google Scholar
  6. 49.
    Kass, M., Witkin, A., & Terzopoulos, D. (1987). Snakes: Active contour models. International Journal of Computer Vision, 1, 321–331.CrossRefGoogle Scholar
  7. 50.
    Xu, J., Chutatape, O., & Chew, P. (2007). Automated optic disk boundary detection by modified active contour model. IEEE Transactions on Biomedical Engineering, 54(3), 473–482.CrossRefGoogle Scholar
  8. 51.
    Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J. P., & Osher, S. (2007). Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision, 28(2), 151–167.MathSciNetCrossRefGoogle Scholar
  9. 52.
    Derraz, F., Beladgham, M., & Khelif, M. (2004, May). Application of active contour models in medical image segmentation. In International conference on information technology: Coding and computing, 2004. Proceedings. ITCC 2004, vol. 2, pp. 675–681.Google Scholar
  10. 53.
    Papandreou, G., & Maragos, P. (2007). Multigrid geometric active contour models. IEEE Transactions on Image Processing, 16(1), 229–240.MathSciNetCrossRefGoogle Scholar
  11. 54.
    Fischer, B., & Modersitzki, J. (2003). Curvature based image registration. Journal of Mathematical Imaging and Vision, 18(1), 81–85.MathSciNetCrossRefGoogle Scholar
  12. 55.
    Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P., & Tannenbaum, A. (1997). A geometric snake model for segmentation of medical imagery. IEEE Transactions on Medical Imaging, 16(2), 199–209.CrossRefGoogle Scholar
  13. 56.
    Wang, G., & Wang, D. (2010). Segmentation of brain MRI image with GVF Snake model. In Proceedings – 2010 1st international conference on pervasive computing, signal processing and applications, PCSPA 2010, pp. 711–714.Google Scholar
  14. 57.
    Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using Matlab – Gonzalez Woods & Eddins.pdf. Education, 624(2), 609.Google Scholar

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© 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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