MRI Image Reconstruction Through Contour Interpolation

  • Bijoyeta RoyEmail author
  • Shivank Goel
  • Mousumi Gupta
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)


Magnetic Resonance Image reconstruction is a promising guidance strategy for acquiring volumetric information from various cross sections of same medical image data. In the field of medical science, visualization of internal anatomy of organs is very important and crucial for proper treatment of any disease. Medical image reconstruction model can help doctors in better visualization of human body organs thus making it easier and convenient for doctors in accurate diagnosis and in therapeutic process. This paper proposed a contour based interpolation technique that will reconstruct an MRI brain dataset for better visualization of patient’s anatomy in order to open up new avenues for the doctors for better analysis of the disease. Main focus was to reconstruct and visualize 3D volumetric brain images which will be helpful for visualizing, manipulating, and quantitatively analyzing human brain anatomy.


Image reconstruction Segmentation 3D view Volume rendering Contour interpolation MRI 


  1. 1.
    Grimson, W.E.L., et al.: Utilizing segmented MRI data in image-guided surgery. Int. J. Pattern Recogn. Artif. Intell. 11(08), 1367–1397 (1997)CrossRefGoogle Scholar
  2. 2.
    Grimson, E., et al.: Clinical experience with a high precision image-guided neurosurgery system. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Heidelberg (1998)Google Scholar
  3. 3.
    Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recogn. 29(8), 1335–1346 (1996)CrossRefGoogle Scholar
  4. 4.
    Vala, H.J., Baxi, A.: A review on Otsu image segmentation algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(2), 387–389 (2013)Google Scholar
  5. 5.
    Qiu, H., Chen, L.T., Qiu, G.P., Zhou, C.: 3D visualization of radar coverage considering electromagnetic interference. WSEAS Trans. Signal Process 10, 460–470 (2014)Google Scholar
  6. 6.
    Levinski, K., Sourin, A., Zagorodnov, V.: 3D visualization and segmentation of brain MRI data. In: GRAPP, pp. 111–118. February 2009Google Scholar
  7. 7.
    Kang, Y., Engelke, K., Kalender, W.A.: Interactive 3D editing tools for image segmentation. Med. Image Anal. 8(1), 35–46 (2004)CrossRefGoogle Scholar
  8. 8.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recogn. 26(9), 1277–1294 (1993)CrossRefGoogle Scholar
  9. 9.
    Sonka, M., Tadikonda, S.K., Collins, S.M.: Knowledge-based interpretation of MR brain images. IEEE Trans. Med. Imag. 15(4), 443–452 (1996)CrossRefGoogle Scholar
  10. 10.
    Liew, A.W.C., Yan, H.: Current methods in the automatic tissue segmentation of 3D magnetic resonance brain images. Curr. Med. Imag. Rev. 2(1), 91–103 (2006)CrossRefGoogle Scholar
  11. 11.
    Joliot, M., Mazoyer, B.M.: Three-dimensional segmentation and interpolation of magnetic resonance brain images. IEEE Trans. Med. Imag. 12(2), 269–277 (1993)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sikkim Manipal Institute of Technology, SMUMajitarIndia

Personalised recommendations