Skip to main content
SpringerLink
Log in

Parametric active contour model-based tumor area segmentation from brain MRI images using minimum initial points

  • Original Article
  • Published:
Iran Journal of Computer Science Aims and scope Submit manuscript

Abstract

Accurate brain tumor segmentation from magnetic resonance imaging (MRI) images is important for proper medication. Manual segmentation may be erroneous and a computer-aided method is recommended for precise segmentation which is also challenging due to the contrast level of MRI images. This research work proposes to utilize a parametric active contour model (PACM)-based deformable snake model to segment brain tumors from MRI images. Conventional PACM model prerequisites some initial points for its initialization which may have a time-consuming issue. The main contribution of this paper is to modify the PACM algorithm, so that it can predict its initial points around the region of interest (ROI) from the given minimum (at least three) initial points. This proposed method aids PACM to find the initial contour points automatically to start the deformable mechanism. Furthermore, different parameters of the PACM algorithm are optimized for the segmentation by check and trial method. The proposed method is applied to different shapes of brain tumors inside the MRI images and found satisfactory segmentation outcomes. Furthermore, the proposed algorithm reports the number of total pixels inside the segmented area. Therefore, we hope that this proposal will help to find the area of critically shaped brain tumor in an MRI image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Ahmmed, R., Rahman, M.A., Hossain, M.F.: An advanced algorithm combining SVM and ANN classifiers to categorize tumors with position from brain MRI images. Adv. Sci. Technol. Eng. Syst. J. 3(2), 40–48 (2018). https://doi.org/10.25046/aj030205

    Article  Google Scholar 

  2. Tandel, G.S., Biswas, M., Kakde, O.G., et al.: A review on a deep learning perspective in brain cancer classification. Cancers (Basel) (2019). https://doi.org/10.3390/cancers11010111

    Article  Google Scholar 

  3. Bi, W.L., Hosny, A., Schabath, M.B., et al.: Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J. Clin. 69(2), 127–157 (2019). https://doi.org/10.3322/caac.21552

    Article  Google Scholar 

  4. Kostka, J.E.A.L.: A review of the medical image segmentation algorithms. In: Peng, S.L., Dey, N., Bundele, M. (eds.) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol. 75. Springer, Singapore (2019)

    Google Scholar 

  5. Jiang, H., He, B., Fang, D., Ma, Z., Yang, B., Zhang, L.: A region growing vessel segmentation algorithm based on spectrum information. Comput. Math. Methods Med. (2013). https://doi.org/10.1155/2013/743870

    Article  MathSciNet  MATH  Google Scholar 

  6. Narkbuakaew, W., Nagahashi, H., Aoki, K., Kubota, Y.: Integration of modified K-means clustering and morphological operations for multi-organ segmentation in CT liver-images. Recent Adv. Biomed. Chem. Eng. Mater. Sci. 59, 34–39 (2014)

    Google Scholar 

  7. Sulaiman, S.N., Mat Isa, N.A.: Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Trans. Consum. Electron. 56(4), 2661–2668 (2010). https://doi.org/10.1109/TCE.2010.5681154

    Article  Google Scholar 

  8. Sun, L., Zhang, S., Chen, H., Luo, L.: Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13(810), 1–9 (2019)

    Google Scholar 

  9. Ahmmed, R., Rahman, M. A., Hossain, M. F.: Fuzzy logic based algorithm to classify tumor categories with position from brain MRI images. In: 3rd International Conference on Electrical Information and Communication Technology (EICT), 7–9 December 2017, KUET, Khulna, Bangladesh (2017)

  10. Lu, Y., Radau, P., Connelly, K., Dick, A., Wright, G.A.: Segmentation of left ventricle in cardiac cine MRI: an automatic image-driven method. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) Functional Imaging and Modeling of the Heart. Lecture Notes in Computer Science, vol. 5528. Springer, Berlin (2009)

    Google Scholar 

  11. Huang, S., Liu, J., Lee, C.L., Venkatesh, K.S., Teo, S.L.L., et al.: An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images. J. Digit. Imaging 24, 598–608 (2011)

    Article  Google Scholar 

  12. Hu, H., Liu, H., Gao, Z., Huang, L.: Hybrid segmentation of left ventricle in cardiac MRI using Gaussian-mixture model and region restricted dynamic programming. Magn. Reson. Imaging 31, 575–584 (2013)

    Article  Google Scholar 

  13. Dakua, P.S.: LV segmentation using stochastic resonance and evolutionary cellular automata. Int. J. Pattern Recognit. Artif. Intell. 29, 1–26 (2015)

    Article  Google Scholar 

  14. Wang, L., Pei, M., Codella, F.C.N., et al.: Left ventricle: fully automated segmentation based on spatio-temporal continuity and myocardium information in cine cardiac magnetic resonance imaging (LV-FAST). Biomed. Res. Int. 36758, 1–9 (2015)

    Google Scholar 

  15. Sanchez-ortiz, I. G.: Medical image computing and computer-assisted intervention-MICCAI’99.1679, (1999)

  16. Suinesiaputra, A., Cowan, R.B., Finn, P.J., et al.: Left ventricular segmentation challenge from cardiac MRI: a collation study. In: Camara, O. (ed.) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Lecture Notes in Computer Science, vol. 7085, pp. 88–97. Springer, New York (2012)

    Google Scholar 

  17. Lebenberg, J., Lalande, A., Clarysse, P., Buvat, I., Casta, C., et al.: Improved estimation of cardiac function parameters using a combination of independent automated segmentation results in cardiovascular magnetic resonance imaging. PLoS ONE 10, e0135715 (2015)

    Article  Google Scholar 

  18. Noman, M. A., Hossain, A. B. M. A., Rahman, M. A.: Initial point prediction based parametric active contour model for left ventricle segmentation of CMRI images. In: International Joint Conference on Computational Intelligence (IJCCI), 14–15 December 2018, Daffodil International University, Dhaka, Bangladesh. pp. 1–06 (2018)

  19. Cheng, K., Xiao, T., Chen, Q., Wang, Y.: Image segmentation using active contours with modified convolutional virtual electric field external force with an edge-stopping function. PLoS ONE 15(3), e0230581 (2020). https://doi.org/10.1371/journal.pone.0230581

    Article  Google Scholar 

  20. Li, G., Li, H.: Robust evolution method of active contour models and application in segmentation of image sequence. J. Electr. Comput. Eng. (2018). https://doi.org/10.1155/2018/3493070

    Article  MathSciNet  Google Scholar 

  21. Mostaar, A., Houshyari, M., Badieyan, S.: Novel active contour model for MRI brain segmentation used in radiotherapy treatment planning. Electron. Phys. 8(5), 2443–2451 (2016). https://doi.org/10.19082/2443

    Article  Google Scholar 

  22. Rabeh, A.B., Benzarti, F., Amiri, H.: Segmentation of brain MRI using active contour model. Int. J. Imaging Syst. Technol. (2017). https://doi.org/10.1002/ima.22205

    Article  Google Scholar 

  23. Hasan, A.M., Meziane, F., Aspin, R., Jalab, H.A.: Segmentation of brain tumors in MRI images using three-dimensional active contour without edge. Symmetry (2016). https://doi.org/10.3390/sym8110132

    Article  MathSciNet  Google Scholar 

  24. Zawish, M., Siyal, A. A., Ahmed, K., Khalil, A., Memon, S.: Brain tumor segmentation in MRI images using Chan-Vese technique in MATLAB. In: International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, 2018, pp. 1–6, (2018). https://doi.org/10.1109/ICECUBE.2018.8610987

  25. Widodo, C.E., Adi, K., Sugiharto, A., Maulana, Q., Pamungkas, A.: Volume target delineation for brain tumor in MRI images using active contour segmentation method. Int. J. Appl. Eng. Res. 11(16), 9031–9036 (2016)

    Google Scholar 

  26. Hsiao, P. Y., Chou, S. S., Huang, F. C.: Generic 2-D Gaussian smoothing filter for noisy image processing. In: TENCON 2007–2007 IEEE Region 10 Conference, Taipei, 2007, pp. 1–4. (2007). https://doi.org/10.1109/TENCON.2007.4428941.

  27. Zhu, Y., Huang, C.: An improved median filtering algorithm for image noise reduction. Phys. Proced. Int. Conf. Solid State Devices Mater. Sci. 25, 609–616 (2012). https://doi.org/10.1016/j.phpro.2012.03.133

    Article  Google Scholar 

  28. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour model. Int. J. Comput. Vis. 1, 321–331 (1988)

    Article  Google Scholar 

  29. Kumar, R.: Snakes: active contour models. MATLAB Central File Exchange. https://www.mathworks.com/matlabcentral/fileexchange/28109-snakes-active-contour-models?focused=5156463&tab=function. (2010)

Download references

Acknowledgements

The authors would like to thank Rasel Ahmmed, Dept. of ECE, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, and Md. Asadur Rahman, PhD, Dept. of Biomedical Engineering, Military Institute of Science and Technology (MIST) for their different guidelines to conduct this research work.

Funding

The authors got no financial aids from any organization for this research work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Dhaka University of Engineering and Technology (DUET), Gazipur, Gazipur, 1707, Bangladesh

    Md. Motiul Islam & Md. Abul Kashem

Authors
  1. Md. Motiul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Md. Abul Kashem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Motiul Islam.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Islam, M.M., Kashem, M.A. Parametric active contour model-based tumor area segmentation from brain MRI images using minimum initial points. Iran J Comput Sci 4, 125–132 (2021). https://doi.org/10.1007/s42044-020-00078-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42044-020-00078-8

Keywords

Search

Navigation