Advertisement

Probabilistic Segmentation of Brain White Matter Lesions Using Texture-Based Classification

  • Mariana BentoEmail author
  • Yan Sym
  • Richard Frayne
  • Roberto Lotufo
  • Letícia Rittner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)

Abstract

Lesions in brain white matter can cause significant functional deficits, and are often associated with neurological disease. The quantitative analysis of these lesions is typically performed manually by physicians on magnetic resonance images and represents a non-trivial, time-consuming and subjective task. The proposed method automatically segments white matter lesions using a probabilistic texture-based classification approach. It requires no parameters to be set, assumes nothing about lesion location, shape or size, and demonstrates better results (Dice coefficient of 0.84) when compared with other, similar published methods.

Keywords

White matter lesion (WML) Magnetic resonance (MR) imaging Brain Segmentation Texture features 

Notes

Acknowledgments

The authors thank FAPESP, CAPES and CNPQ for their financial support.

References

  1. 1.
    Appenzeller, S., Li, L.M., Faria, A.V., Costallat, L.T., Cendes, F.: Quantitative magnetic resonance imaging analyses and clinical significance of hyperintense white matter lesions in systemic lupus erythematosus patients. Ann. Neurol. 64(6), 635–643 (2008)CrossRefGoogle Scholar
  2. 2.
    Vernooij, M.W., Arfan Ikram, M., Tanghe, H.L., Vincent, A.J.P.E., Hofman, A., Krestin, G.P., Niessen, W.J., Breteler, M.M.B., Lugt, A.: Incidental findings on brain MRI in the general population. New Engl. J. Med. 357(18), 1821–1828 (2007)CrossRefGoogle Scholar
  3. 3.
    Despotovic, I., Goossens, B., Philips, W.: MRI segmentation of the human brain: challenges, methods, and applications. Comput. Math. Methods in Med. 2015(1), 1–23 (2015)CrossRefGoogle Scholar
  4. 4.
    Roura, E., Oliver, A., Cabezas, M., Valverde, S., Pareto, D., Vilanova, J., Ramió-Torrentà, L., Rovira, A., Lladó, X.: A toolbox for multiple sclerosis lesion segmentation. Neuroradiology 57(10), 1031–1043 (2015)CrossRefGoogle Scholar
  5. 5.
    Oppedal, K., Eftestol, T., Engan, K., Beyer, M., Aarsland, D.: Classifying dementia using local binary patterns from different regions in magnetic resonance images. Int. J. Biomed. Imaging 1–14, 2015 (2015)Google Scholar
  6. 6.
    Loizou, C., Pantziaris, M., Pattichis, C., Seimenis, I.: Brain MR image normalization in texture analysis of multiple sclerosis. J. Biomed. Graph. Comput. 3(1), 20 (2013)Google Scholar
  7. 7.
    Kloppel, S., Abdulkadir, A., Hadjidemetriou, S., Issleib, S., Frings, L., Thanh, T.N., Mader, I., Teipel, S.J., Hull, M., Ronnebeger, O.: A comparison of different automated methods for the detection of white matter lesions in MRI data. Neuroimage 57(2), 416–422 (2011)CrossRefGoogle Scholar
  8. 8.
    Steenwijk, M., Pouwels, P., Daams, M., Dalen, J., Caan, M., Richard, E., Barkhof, F., Vrenken, H.: Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). NeuroImage: Clin. 3, 462–469 (2013)CrossRefGoogle Scholar
  9. 9.
    Anbeek, P., Vincken, K.L., Osch, M.J.P., Bisschops, R.H.C., Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21(3), 1037–1044 (2004)CrossRefGoogle Scholar
  10. 10.
    Maier, O., Menze, B.H., von der Gablentz, J., Häni, L., Heinrich, M.P., Liebrand, M., Winzeck, S., Basit, A., Bentley, P., Chen, L., Christiaens, D., Dutil, F., Egger, K., Feng, C., Glocker, B., Götz, M., Haeck, T., Halme, H.L., Havaei, M., Iftekharuddin, K.M., Jodoin, P.M., Kamnitsas, K., Kellner, E., Korvenoja, A., Larochelle, H., Ledig, C., Lee, J.H., Maes, F., Mahmood, Q., Maier-Hein, K.H., McKinley, R., Muschelli, J., Pal, C., Pei, L., Rangarajan, J.R., Reza, S.M., Robben, D., Rueckert, D., Salli, E., Suetens, P., Wang, C.W., Wilms, M., Kirschke, J.S., Krämer, U.M., Münte, T.F., Schramm, P., Wiest, R., Handels, H., Reyes, M.: ISLES 2015 - a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250–269 (2017)CrossRefGoogle Scholar
  11. 11.
    Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  12. 12.
    Lu, Q., Gobbi, D., Frayne. R., Salluzzi, M.: Cerebra-WML: a stand-alone application for quantification of white matter lesion. In: Proceedings of Imaging Network Ontario Symposium (2014)Google Scholar
  13. 13.
    Griffanti, L., Zamboni, G., Khan, A., Li, L., Bonifacio, G., Sundaresan, V., Schulz, U., Kuker, W., Battaglini, M., Rothwell, P., Jenkinson, M.: BIANCA (Brain Intensity Abnormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities. NeuroImage 141, 191–205 (2016)CrossRefGoogle Scholar
  14. 14.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(1), 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Woods, R., Gonzalez, R.C.: Digital Image Processing. Edgard Blucher, São Paulo (2000)Google Scholar
  16. 16.
    Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 195–208. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_17 CrossRefGoogle Scholar
  17. 17.
    Kamnitsas, K., Chen, L., Ledig, C., Rueckert, D., Glocker, B.: Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI. In: Proceedings of Ischemic Stroke Lesion Segmentation Challenge, Held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention 2015 (2015)Google Scholar
  18. 18.
    Chen, L., Bentley, P., Rueckert, D.: A novel framework for sub-acute stroke lesion segmentation based on random forest. In: Proceedings of Ischemic Stroke Lesion Segmentation Challenge, Held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention 2015 (2015)Google Scholar
  19. 19.
    Feng, C., Zhao, D., Huang, M.: Segmentation of stroke lesions in multi-spectral MR images using bias correction embedded FCM and three phase level set. In: Proceedings of Ischemic Stroke Lesion Segmentation Challenge, Held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention 2015 (2015)Google Scholar
  20. 20.
    Halme, H.-L., Korvenoja, A., Salli, E.: ISLES (SISS) challenge 2015: segmentation of stroke lesions using spatial normalization, random forest classification and contextual clustering. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 211–221. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_18 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mariana Bento
    • 1
    • 3
    Email author
  • Yan Sym
    • 1
  • Richard Frayne
    • 2
    • 3
    • 4
  • Roberto Lotufo
    • 1
  • Letícia Rittner
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringUniversity of CampinasCampinasBrazil
  2. 2.Radiology and Clinical Neuroscience, Hotchkiss Brain InstituteUniversity of CalgaryCalgaryCanada
  3. 3.Calgary Image Processing and Analysis CentreFoothills Medical CentreCalgaryCanada
  4. 4.Seaman Family MR Research CentreFoothills Medical CentreCalgaryCanada

Personalised recommendations