Skip to main content

Advertisement

SpringerLink
Log in

A new Probabilistic Active Contour region-based method for multiclass medical image segmentation

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed.

Graphical Abstract

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

Similar content being viewed by others

References

  1. Kass M, Witkin A, Terzopoulus D (1998) Snakes: active contour models. Int J Comput Vis 4:321–331

    Google Scholar 

  2. Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10:266–277

    Article  PubMed  CAS  Google Scholar 

  3. Vese L, Chan T (2002) A multiphase level set framework for image segmentation using the mumford and shah mode. Int J Comput Vis 50:271–293

    Article  Google Scholar 

  4. Kim J, Fisher J, Yezzi A, Cetin M, Willsky A (2005) A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans Med Imaging 14:1486–1502

    Google Scholar 

  5. Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72:195–215

    Article  Google Scholar 

  6. Joshi N, Brady M (2010) Non-parametric mixture model based evolution of level sets applications to medical images. Int J Comput Vis 88:52–68

    Article  Google Scholar 

  7. Shi N, Pan J (2016) An improved active contours model for image segmentation by level set method. Optik-Int J Light Electron Opt 127(3):1037–1042

    Article  Google Scholar 

  8. Li C, Kao C, Gore J, Ding Z (2008) Minimization of region scalable fitting energy for image segmentation. IEEE Trans Image Process 17:1940–1949

    Article  PubMed  PubMed Central  Google Scholar 

  9. Mukherjee S, Acton S (2015) Region based segmentation in presence of intensity inhomogeneity using Legendre polynomials. IEEE Signal Process Lett 22:298–302

    Article  Google Scholar 

  10. Niu S, Chen Q, de Sisternes L et al (2017) Robust noise region-based active contour model via local similarity factor for image segmentation. Pattern Recogn 61:104–119

  11. Van-Truong P, Thi-Thao T (2016) Active contour model and nonlinear shape priors with application to left ventricle segmentation in cardiac MR images. Optik 127:991–1002

    Article  Google Scholar 

  12. Dahdou S, Angelini E, Grange B, Blocha I (2015) Segmentation of embryonic and fetal 3D ultrasound images based on pixel intensity distributions and shape priors. Med Image Anal 24:255–268

    Article  Google Scholar 

  13. Xie X, Wang C, Zhang A, Meng X (2014) A robust level set method based on local statistical information for noisy image segmentation. Optik 125:2199–2204

    Article  Google Scholar 

  14. Wang L, Chen Y, Pan X, Hong X, Xia D (2010) Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy. J Neurosci Methods 188:316–325

    Article  PubMed  Google Scholar 

  15. Wang L, Li C, Sun Q, Xia D, Kao C (2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput Med Imaging Graph 33:520–531

    Article  PubMed  Google Scholar 

  16. Zhou S, Wang J, Zhang S et al (2016) Active contour model based on local and global intensity information for medical image segmentation. Neurocomputing 186:107–118

    Article  Google Scholar 

  17. Wang Y, Liatsis P (2012) Automatic segmentation of coronary arteries in CT imaging in the presence of kissing vessel artifacts. IEEE Trans Inf Technol Biomed 16:782–788

    Article  PubMed  Google Scholar 

  18. Ilunga-Mbuyamba E, Cruz-Duarte JM, Avina-Cervantes JG et al (2016) Active contours driven by Cuckoo search strategy for brain tumour images segmentation. Expert Syst Appl 56:59–68

    Article  Google Scholar 

  19. Yazdanpanah A, Hamarneh G, Smith B, Sarunic M (2011) Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach. IEEE Trans Med Imaging 30:484–496

    Article  PubMed  Google Scholar 

  20. Mumford D, Shah J (1989) Optimal approximation by piecewise smooth functions and associated variational problems. Comm Pure Appl Math 42:577–685

    Article  Google Scholar 

  21. Li S (2009) Markov random field modeling in image analysis, 3rd edn. Springer, Berlin

    Google Scholar 

  22. Duda R, Hart P, David G (2001) Pattern classification, 2nd edn. Wiley, New York

    Google Scholar 

  23. Kwan R, Evans A, Pike G (1999) MRI Simulation-based evaluation of image-processing and classification methods. IEEE Trans Med Imaging 18:1085–1097

    Article  PubMed  CAS  Google Scholar 

  24. Getreuer P, Chan T, Vese L (2012) Segmentation. Image Process Line 2:214–224

    Article  Google Scholar 

  25. Gudbjartsson H, Patz S (1995) The rician distribution of noisy MRI data. Magn Reson Med 34:910–914

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. The Internet Brain Segmentation Repository (IBSR). Available online: http://www.cma.mgh.harvard.edu/ibsr/

  27. Dice L (1945) Measures of the amount of ecologic association between species. Ecol 26:297–302

    Article  Google Scholar 

  28. Heimann T, Van Ginneken B, Styner MA, et al. (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251–1265

    Article  PubMed  Google Scholar 

  29. Ahmadvand A, Reza M (2015) Improving the runtime of MRF based method for MRI brain segmentation. Appl Math Comput 256:808–818

    Google Scholar 

  30. Pereira A, Pinto A, Oliveira J, Mendrik A, Correia J, Silva C (2016) Automatic brain tissue segmentation in MR images using random forests and conditional random fields. J Neurosci Methods 270:111–123

    Article  PubMed  Google Scholar 

  31. Mahmood Q, Chodorowski A, Persson M (2015) Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps. Innov Res BioMed Eng (IRBM) 36:185–196

    Google Scholar 

Download references

Funding

Arce-Santana was supported by CONACyT through sabbatical year grant no. 625699.

Author information

Authors and Affiliations

  1. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México

    Edgar R. Arce-Santana, Aldo R. Mejia-Rodriguez, Enrique Martinez-Peña, Alfonso Alba & Martin Mendez

  2. Institute of Molecular Bioimaging and Physiology (IBFM)-CNR, Milan, Italy

    Elisa Scalco, Alfonso Mastropietro & Giovanna Rizzo

Authors
  1. Edgar R. Arce-Santana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aldo R. Mejia-Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enrique Martinez-Peña
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alfonso Alba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Mendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elisa Scalco
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alfonso Mastropietro
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giovanna Rizzo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edgar R. Arce-Santana.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arce-Santana, E.R., Mejia-Rodriguez, A.R., Martinez-Peña, E. et al. A new Probabilistic Active Contour region-based method for multiclass medical image segmentation. Med Biol Eng Comput 57, 565–576 (2019). https://doi.org/10.1007/s11517-018-1896-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1896-y

Keywords

Search

Navigation