Multiphase Level Set Segmentation for ROI Extraction from Dental Radiographs

  • Kavindra R. Jain
  • N. C. Chauhan


Image guided surgery, quantitative analysis and visual understanding along with proper medical interpretation have led to the development of segmentation techniques in image processing in a more precise manner. The deformable models provide an explicit representation of the boundary and shape of the object. Various features like inherent connectivity and smoothness which counteract noise and boundary irregularities are present in such models. Based on the region of interest they incorporate knowledge of the nearby regions. While using parametric model we faced certain pitfalls. Firstly, the conditions when the initial model and region of interest boundary differ greatly in size and shape, resulting in high error rate and lesser accuracy. Secondly, it was observed that when region of interest is large, then the model has to be applied more than once separately for each case. To overcome such situations, the level set deformable models also referred as geometric deformable model which provide an elegant solution to address the primary limitation of parametric deformable model. It includes no parameterisation of the contour, topological flexibility and good numerical stability. The results produced by the proposed method were compared with the ground truth results obtained with two domain experts. Based on the evaluation measures, the proposed method is found to be very effective in identification of interested region. The proposed method can be useful for assistance to the dental medical practitioners during their endodontic treatment of the tooth. The method can further be evaluated rigorously and can also be integrated in any computer based diagnostic tool.


Image segmentation Level set method Single phase level set segmentation Multiphase level set segmentation Vitality detailing Bunching property Gradient descent method Contouring Deformable models Pixel connectivity Parametric model 


  1. 58.
    Tejaswi, P., & Subhani, S. K. M. (2014). A region based variational method for image segmentation and bias correction with application to MRI. International Journal of Scientific Engineering and Research, 2(9), 40–46.Google Scholar
  2. 59.
    Terzopoulos, D., & Fleischer, K. (1988). Deformable models. The Visual Computer, 4, 306–331.CrossRefGoogle Scholar
  3. 60.
    Zhang, K., Zhang, L., Song, H., & Zhou, W. (2010). Active contours with selective local or global segmentation: A new formulation and level set method. Image and Vision Computing, 28(4), 668–676.CrossRefGoogle Scholar
  4. 61.
    Osher, S., & Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79(1), 12–49.MathSciNetCrossRefGoogle Scholar
  5. 62.
    Chen, S., & Radke, R. J. (2009) Level set segmentation with both shape and intensity priors. In Proceedings of the IEEE international conference on computer vision, pp. 763–770.Google Scholar
  6. 63.
    Cootes, T. F., Hill, A., Taylor, C. J., & Haslam, J. (1994). Use of active shape models for locating structures in medical images. Image and Vision Computing, 12(6), 355–365.CrossRefGoogle Scholar
  7. 64.
    Yushkevich, P., Piven, J., Cody, H., & Ho, S. (2006). User-guided level set segmentation of anatomical structures with ITK-SNAP. NeuroImage, 31(3), 1116–1128.CrossRefGoogle Scholar
  8. 65.
    Pock, T., Cremers, D., Bischof, H., & Chambolle, A. (2009). An algorithm for minimizing the Mumford-Shah functional. In Proceedings of the IEEE international conference on computer vision, pp. 1133–1140.Google Scholar
  9. 66.
    Vese, L. A., & Chan, T. F. (2002). A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision, 50(3), 271–293.CrossRefGoogle Scholar
  10. 67.
    Li, B. N., Chui, C. K., Chang, S., & Ong, S. H. (2011). Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine, 41(1), 1–10.CrossRefGoogle Scholar
  11. 68.
    Jomier, J., Rault, E., & Aylward, S. R. (2004). Automatic Quantification of Pupil Dilation Under Stress. International Symposium on Biomedical Imaging (ISBI), 249–252.Google Scholar
  12. 69.
    Zhan, T., Zhang, J., Xiao, L., Chen, Y., & Wei, Z. (2013). An improved variational level set method for MR image segmentation and bias field correction. Magnetic Resonance Imaging, 31(3), 439–447.CrossRefGoogle Scholar
  13. 70.
    Qian Zhao, Y., Hong Wang, X., Fang Wang, X., & Shih, F. Y. (2014). Retinal vessels segmentation based on level set and region growing. Pattern Recognition, 47(7), 2437–2446.CrossRefGoogle 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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