Skip to main content
SpringerLink
Log in

Ensemble Classification Using Entropy-Based Features for MRI Tissue Segmentation

  • Conference paper
  • First Online:
Pattern Recognition and Artificial Intelligence (MedPRAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1322))

  • 541 Accesses

Abstract

It is still hard to deal with artifacts in magnetic resonance images (MRIs), particularly when the latter are to be segmented. This paper introduces a novel feature, namely the spatial entropy of intensity that allows a pattern-based representation which enhances the MRI segmentation despite presence of high levels of noise and intensity non uniformity (INU) within MRI data. Moreover, we bring out that ensembles of classifiers used with the proposed feature have significantly enhanced structured MRI segmentation. Thus, to conduct experiments, MRIs with different artifact levels were extracted and exploited from the Brain Web MRI database. The obtained results reveal that the proposed feature, especially when used with ensembles of classifiers has significantly enhanced the overall MRI segmentation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ashburner, J., Friston, K.J.: Unified segmentation (2005)

    Google Scholar 

  2. Bahadure, N.B., Ray, A.K., Thethi, H.P.: Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imaging 9749108:1–9749108:12 (2017). https://doi.org/10.1155/2017/9749108

  3. Bezdek, J., Ehrlich, R., Full, W.E.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)

    Article  Google Scholar 

  4. de Brébisson, A., Montana, G.: Deep neural networks for anatomical brain segmentation. CoRR abs/1502.02445 (2015). http://arxiv.org/abs/1502.02445

  5. Bringmann, B., Nijssen, S., Zimmermann, A.: Pattern-based classification: a unifying perspective. CoRR abs/1111.6191 (2011). http://arxiv.org/abs/1111.6191

  6. Caldairou, B., Passat, N., Habas, P., Studholme, C., Rousseau, F.: A non-local fuzzy segmentation method: application to brain MRI. Pattern Recogn. 44(9), 1916–1927 (2011). https://doi.org/10.1016/j.patcog.2010.06.006, https://hal.archives-ouvertes.fr/hal-00476587

  7. Cocosco, C., Kollokian, V., Kwan, R.S., Evans, A.: Simulated brain database homepage. https://brainweb.bic.mni.mcgill.ca/brainweb. Accessed 03 June 2020

  8. Freund, Y., Schapire, R.: A short introduction to boosting (1999)

    Google Scholar 

  9. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Networks 4(2), 251–257 (1991). https://doi.org/10.1016/0893-6080(91)90009-T

  10. Jenkinson, M.: Bet2 : MR-based estimation of brain, skull and scalp surfaces. In: Eleventh Annual Meeting of the Organization for Human Brain Mapping (2005). https://ci.nii.ac.jp/naid/10030066593/en/

  11. Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman Soulié, F., Hérault, J. (eds.) Neurocomputing: Algorithms, Architectures and Applications. NATO ASI Series, vol. F68, pp. 41–50. Springer-Verlag, Heidelberg (1990). https://doi.org/10.1007/978-3-642-76153-9_5

    Chapter  Google Scholar 

  12. Li, S.: Markov random field modeling in image analysis. In: Advances in Pattern Recognition (2009)

    Google Scholar 

  13. Oliveira., G.C., Varoto., R., Jr., A.C.: Brain tumor segmentation in magnetic resonance images using genetic algorithm clustering and adaboost classifier. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies. BIOIMAGING: BIOIMAGING, vol. 2, pp. 77–82. INSTICC, SciTePress (2018). https://doi.org/10.5220/0006534900770082

  14. Park, J., Sandberg, I.W.: Approximation and radial-basis-function networks. Neural Comput. 5(2), 305–316 (1993). https://doi.org/10.1162/neco.1993.5.2.305

  15. Pham, T.X., Siarry, P., Oulhadj, H.: A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods. Magn. Resonance Imaging 61, 41–65 (2019)

    Article  Google Scholar 

  16. Rajasree, R., Columbus, C.C.: Brain tumour image segmentation and classification system based on the modified adaboost classifier. Int. J. Appl. Eng. Res. 10(14) (2015)

    Google Scholar 

  17. Richard, N., Dojat, M., Garbay, C.: Distributed markovian segmentation: application to MR brain scans. Pattern Recogn. 40(12), 3467–3480 (2007). https://doi.org/10.1016/j.patcog.2007.03.019

  18. Roma, A.A., et al.: Invasive endocervical adenocarcinoma: a new pattern-based classification system with important clinical significance. Am. J. Surg. Pathol. 39(5), 667–672 (2015). https://doi.org/10.1097/pas.0000000000000402

  19. Saritha, M., Paul Joseph, K., Mathew, A.T.: Classification of mri brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn. Lett. 34(16), 2151–2156 (2013). https://doi.org/10.1016/j.patrec.2013.08.017

  20. Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: Distributed local MRF models for tissue and structure brain segmentation. IEEE Trans. Med. Imaging 28(8), 1278–1295 (2009)

    Article  Google Scholar 

  21. Schölkopf, B., Burges, C., Vapnik, V.: Extracting support data for a given task. In: KDD (1995)

    Google Scholar 

  22. Smith, S.: Fast robust automated brain extraction. Human Brain Mapp. 17 (2002)

    Google Scholar 

  23. Yamanakkanavar, N., Choi, J.Y., Lee, B.: MRI segmentation and classification of human brain using deep learning for diagnosis of Alzheimer’s disease: a survey. Sensors 20(11), 3243 (2020). https://doi.org/10.3390/s20113243

  24. Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)

    Article  Google Scholar 

  25. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001)

    Article  Google Scholar 

  26. Zhou, C., Cule, B., Goethals, B.: Pattern based sequence classification. IEEE Trans. Knowl. Data Eng. 28, 1285–1298 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LICUS Lab, University 20 Aout 1955, BP 26 Route el Hadaik, 21000, Skikda, Algeria

    Nadjet Bouchaour & Smaine Mazouzi

Authors
  1. Nadjet Bouchaour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Smaine Mazouzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadjet Bouchaour .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Digital Research Center of Sfax, Sfax, Tunisia

    Yousri Kessentini

  3. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  4. Digital Research Centre of Sfax, Sfax, Tunisia

    Mohamed Jmaiel

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bouchaour, N., Mazouzi, S. (2021). Ensemble Classification Using Entropy-Based Features for MRI Tissue Segmentation. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71804-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71803-9

  • Online ISBN: 978-3-030-71804-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation