Semi-automatic active contour-based segmentation to remove eyes, meninges, and skull from MRI



Brain imaging acquisition can present different issues, such as noisy images which can result in a problematic diagnosis. Image preparation such as skull stripping and region segmentation is a fundamental step in order to support a better medical diagnosis outcome. Therefore, this study presented a segmentation technique based on the active contour model to perform skull stripping.


The method is applied on the neuroimaging database available by the OASIS neuroimaging dataset. The method proposed here uses active contour model followed by k-means clustering technique in order to converge to a locally minimal energy value which can be equivalent to the brain tissue area aiming to avoid loss of image quality and brain structures. Statistical analysis was also performed in order to determine how image texture characteristics were affected.


The active contour method achieved results within the ones presented on the state-of-art values segmentation methods with 96.4% of sensitivity and 96% of specificity using only 4 k-means clusters. Image texture characteristics such as entropy and correlation presented values of 1.8804 and 0.96, respectively.


The high entropy value accentuated the gray-level contrast and highlighted anatomical structures for brain visualization. These high evaluation scores demonstrate that the semi-automatic contour-based segmentation algorithm is a powerful tool for segmentation and skull-stripping decreasing loss of image quality and brain structures.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. Chen K, Shen J, Scalzo F. Skull stripping using confidence segmentation convolution neural network. Lect Notes Comput Sci. 2018.

  2. De Bresser J, Portegies MP, Leemans A, Biessels GJ, Kappelle LJ, Viergever MA. A comparison of MR based segmentation methods for measuring brain atrophy progression. NeuroImage. 2011;54:760–8.

    Article  Google Scholar 

  3. Dey R, Hong Y. CompNet: complementary segmentation network for brain MRI extraction. Springer: International Conference on Medical Image Computing and Computer-Assisted Intervention; 2018.

    Google Scholar 

  4. Duay V.; Bresson, X.; Castro J.S.; Pollo C.; Cuadra M.B.; Thiran JP. An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation. Med Image Comput Comput Assist Interv. 2008;11(Pt 2):980-8. Ali.

  5. Essadike A, Ouabida E, Bouzid A. Brain tumor segmentation with Vander Lugt correlator based active contour. Comput Methods Prog Biomed. 2018;160:103–17.

    Article  Google Scholar 

  6. Fischl B. FreeSurfer. FreeSurfer Neuroimage. 2012;62:774–81. Epub 2012 Jan 10.

    Article  Google Scholar 

  7. Franke K, Gaser C. Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s disease. GeroPsych. 2012.

  8. Friston, K. J., Ashburner, J., Frith, C. D., Poline, J.-B., Heather, J. D., amp; Frackowiak, R. S. J. Spatial registration and normalization of images. Human Brain Mapping, 1995.

  9. Haralick RM, Shanmugam K, Dinstein I. Texture features for image classification. IEEE Trans Systems Man Cybem. 1973;SMC-3:610–21.

    Article  Google Scholar 

  10. Iglesias JE, Cheng-Yi L, Thompson PM, Zhuowen T. Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods. IEEE Transactions on Medical Imaging. 2011;30:1617–34.

    Article  Google Scholar 

  11. Ikram MA, Vrooman HA, Vernooij MW, van der Lijn F, Hofman A, van der Lugt A, et al. Brain tissue volumes in the general elderly population. Neurobiol Aging. 2008;29:882–90.

    Article  Google Scholar 

  12. Kalavathi P, Prasath VBS. Methods on skull stripping of MRI head scan images—a review. J Digit Imaging. 2015;29:365–79.

    Article  Google Scholar 

  13. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis. 1998;1:321–31.

    Article  Google Scholar 

  14. Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, et al. Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage. 2016;129:460–9.

    Article  Google Scholar 

  15. Klauschen F, Goldman A, Barra V, Meyer-Lindenberg A, Lundervold A. Evaluation of automated brain MR image segmentation and volumetry methods. Hum Brain Mapp. 2009;30:1310–27.

    Article  Google Scholar 

  16. LaMontagne PJ, Benzinger TLS, Morris JC, Keefe S, Hornbeck R, Xiong C, et al. OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer’s disease. Alzheimer's & Dementia: The Journal of the Alzheimer's Association. 2018.

  17. Peng SL, Chen CF, Liu HL, Lui CC, Huang YJ, Lee TH, et al. Analysis of parametric histogram from dynamic contrast-enhanced MRI: application in evaluating brain tumor response to radiotherapy. NMR Biomed. 2012;26:443–50.

    Article  Google Scholar 

  18. Price K. Anything you can do, I can do better (no you can’t). Computer Vision Graphics and Image Processing. 1998;36:387–91.

    Article  Google Scholar 

  19. Roy S, Maji P. An accurate and robust skull stripping method for 3-D magnetic resonance brain images. Magn Reson Imaging. 2018;54:46–57.

    Article  Google Scholar 

  20. Roy S, Butman JA, Pham DL. Robust skull stripping using multiple MR image contrasts insensitive to pathology. NeuroImage. 2017;146:132–47.

    Article  Google Scholar 

  21. Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM. Magnetic resonance image tissue classification using a partial volume model. NeuroImage. 2001;13:856–76.

    Article  Google Scholar 

  22. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17:143–55.

    Article  Google Scholar 

  23. Wang J, Kong J, Lu Y, Qi M, Zhang B. A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph. 2008;32:685–98.

    Article  Google Scholar 

  24. Zaidi H, Ruest T, Schoenahl F, & Montandon M.-L. Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET. NeuroImage, 2006.

  25. Zhang W-L, Wang X-Z. Feature Extraction and Classification for Human Brain CT Images. 2007 International Conference on Machine Learning and Cybernetics. 2007.

Download references


Authors would like to acknowledge OASIS Brains Project by the data provided (OASIS-3: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448, R01EB009352. AV-45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly), Federal University of Rio Grande do Norte (UFRN), Santos Dumont Institute (ISD) and Ministry of Education (MEC).

Author information



Corresponding author

Correspondence to José Micael Delgado Barbosa.

Ethics declarations

Conflict of interest

José Micael Delgado Barbosa, Tassia Luiza Gonçalves Magalhães Nunes, Tâmara Luiza Gonçalves Magalhães Nunes, Abner Cardoso Rodrigues Neto, and Edgard Morya do not have any conflict of interest related to the present paper and data used to conduct the research.

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

Verify currency and authenticity via CrossMark

Cite this article

Barbosa, J.M.D., Nunes, T.L.G.M., Nunes, T.L.G.M. et al. Semi-automatic active contour-based segmentation to remove eyes, meninges, and skull from MRI. Res. Biomed. Eng. (2020).

Download citation


  • Brain imaging
  • Skull stripping
  • Active contour model
  • Clustering
  • Image texture