Advertisement

Epileptogenic Lesion Quantification in MRI Using Contralateral 3D Texture Comparisons

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

Epilepsy is a disorder of the brain that can lead to acute crisis and temporary loss of brain functions. Surgery is used to remove focal lesions that remain resistant to treatment. An accurate localization of epileptogenic lesions has a strong influence on the outcome of epilepsy surgery. Magnetic resonance imaging (MRI) is clinically used for lesion detection and treatment planning, mainly through simple visual analysis. However, visual inspection in MRI can be highly subjective and subtle 3D structural abnormalities are not always entirely removed during surgery. In this paper, we introduce a lesion abnormality score based on computerized comparison of the 3D texture properties between brain hemispheres in T1 MRI. Overlapping cubic texture blocks extracted from user–defined 3D regions of interest (ROI) are expressed in terms of energies of 3D steerable Riesz wavelets. The abnormality score is defined as the Hausdorff distance between the ROI and its corresponding contralateral region in the brain, both expressed as ensembles of blocks in the feature space. A classification based on the proposed score allowed an accuracy of 85% with 10 control subjects and 8 patients with epileptogenic lesions. The approach therefore constitutes a valuable tool for the objective pre–surgical evaluation of patients undergoing epilepsy surgery.

Keywords

3D texture Riesz transform epilepsy CAD 
Download to read the full conference paper text

References

  1. 1.
    Antel, S.B., Collins, D.L., Bernasconi, N., Andermann, F., Shinghal, R., Kearney, R.E., Arnold, D.L., Bernasconi, A.: Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis. NeuroImage 19(4), 1748–1759 (2003)CrossRefGoogle Scholar
  2. 2.
    Bastos, A., Corneau, R.M., Andermann, F., Melanson, D., Cendes, F., Dubeau, F., Fontaine, S., Tampieri, D., Olivier, A.: Diagnosis of subtle focal dysplastic lesions: curvilinear reformatting from three-dimensional magnetic resonance imaging. Annals of Neurology 46, 88–94 (1999)CrossRefGoogle Scholar
  3. 3.
    Bergo, F.P.G., Falcão, A.X., Yasuda, C.L., Cendes, F.: FCD segmentation using texture asymmetry of MR–T1 images of the brain. In: 5th IEEE International Symposium on Biomedical Imaging, pp. 424–427 (2008)Google Scholar
  4. 4.
    Bernasconi, A., Bernasconi, N.: Unveiling epileptogenic lessions: The contribution of image processing. Epilepsia 52, 20–24 (2011)CrossRefGoogle Scholar
  5. 5.
    Besson, P., Andermann, F., Dubeau, F., Bernasconi, A.: Small focal cortical dysplasia lesions are located at the bottom of a deep sulcus. Brain 131(12), 3246–3255 (2008)CrossRefGoogle Scholar
  6. 6.
    Chenouard, N., Unser, M.: 3D steerable wavelets and monogenic analysis for bioimaging. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 2132–2135 (April 2011)Google Scholar
  7. 7.
    Colliot, O., Bernasconi, N., Khalili, N., Antel, S.B., Naessens, V.B., Bernasconi, A.: Individual voxel-based analysis of gray matter in focal cortical dysplasia. Neuroimage 29(1), 162–171 (2006)CrossRefGoogle Scholar
  8. 8.
    Depeursinge, A., Foncubierta-Rodríguez, A., Vargas, A., Van De Ville, D., Platon, A., Poletti, P.A., Müller, H.: Rotation–covariant texture analysis of 4D dual–energy CT as an indicator of local pulmonary perfusion. In: IEEE 10th International Symposium on Biomedical Imaging, ISBI 2013, pp. 149–152. IEEE (April 2013)Google Scholar
  9. 9.
    Focke, N.K., Yogarajah, M., Symms, M.R., Gruber, O., Paulus, W., Duncan, J.S.: Automated MR image classification in temporal lobe epilepsy. Neuroimage 59(1), 356–362 (2012)CrossRefGoogle Scholar
  10. 10.
    Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for ms lesion segmentation in multi–channel magnetic resonance images. NeuroImage 57, 378–390 (2011)CrossRefGoogle Scholar
  11. 11.
    Huppertz, H.J., Grimm, C., Fauser, S., Kassubek, J., Mader, I., Hochmuth, A., Spreer, J., Schulze-Bonhage, A.: Enhanced visualization of blurred gray-white matter junctions in focal cortical dysplasia by voxel–based 3D MRI analysis. Epilepsy Research 67(1), 35–50 (2005)CrossRefGoogle Scholar
  12. 12.
    Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)CrossRefGoogle Scholar
  13. 13.
    Kassner, A., Thornhill, R.E.: Texture analysis: A review of neurologic MR imaging applications. American Journal of Neuroradiology 31, 809–816 (2010)CrossRefGoogle Scholar
  14. 14.
    Klein, S., Pluim, J.P., Staring, M., Viergever, M.A.: Adaptive stochastic gradient descent optimisation for image registration. International Journal of Computer Vision 81(3), 227–239 (2009)CrossRefGoogle Scholar
  15. 15.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity–based medical image registration. IEEE Transactions on Medical Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  16. 16.
    Mellerio, C., Labeyrie, M.A., Chassoux, F., Daumas-Duport, C., Landré, E., Turak, B., Roux, F.X., Meder, J.F., Devaux, B., Oppenheim, C.: Optimizing MR imaging detection of type 2 focal cortical dysplasia: Best criteria for clinical practice. American Journal of Neuroradiology 33(10), 1932–1938 (2012)CrossRefGoogle Scholar
  17. 17.
    Montenegro, M.A., Min Li, L., Guerreiro, M.M., Guerreiro, C.A.M., Fernando, C.: Focal cortical dysplasia: Improving diagnosis and localization with magnetic resonance imaging multiplanar and curvilinear reconstruction. Journal of Neuroimaging 12, 224–230 (2002)Google Scholar
  18. 18.
    Palmini, A., Gambardella, A., Andermann, F., Dubeau, F., da Costa, J.C., Olivier, A., Tampieri, D., Gloor, P., Quesney, F., Andermann, E., Paglioli, E., Paglioli-Neto, E., Andermann, L.C., Leblanc, R., Kim, H.I.K.: Intrinsic epileptogenicity of human dysplastic cortex as suggested by corticography and surgical results. Annals of Neurology 37, 476–487 (1995)CrossRefGoogle Scholar
  19. 19.
    Riney, C.J., Chong, W.K., Clark, C.A., Cross, J.H.: Voxel based morphometry of FLAIR MRI in children with intractable focal epilepsy: Implications for surgical intervention. European Journal of Radiology 81, 1299–1305 (2012)CrossRefGoogle Scholar
  20. 20.
    Tassi, L., Colombo, N., Garbelli, C.R., Francione, S., Lo Russo, G., Mai, R., Cardinale, F., Cossu, M., Ferrario, A., Galli, C., Bramerio, M., Citterio, A., Spreafico, R.: Focal cortical dysplasia: neuropathological subtypes, EEG, neuroimaging and surgical outcome. Brain 125(8), 1719–1732 (2002)CrossRefGoogle Scholar
  21. 21.
    Unser, M.: Sum and difference histograms for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(1), 118–125 (1986)CrossRefGoogle Scholar
  22. 22.
    Wagner, J., Weber, B., Urbach, H., Elger, C.E., Huppertz, H.J.: Morphometric MRI analysis improves detection of focal cortical dysplasia type II. Brain 134, 2844–2854 (2011)CrossRefGoogle Scholar
  23. 23.
    Woermann, F.G., Free, S.L., Koepp, M.J., Ashburner, J., Duncan, J.S.: Voxel-by-voxel comparison of automatically segmented cerebral gray matter — a rater–independent comparison of structural MRI in patients with epilepsy. Neuroimage 10(4), 373–384 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of Applied Sciences Western Switzerland (HES–SO)Switzerland
  2. 2.University and University Hospitals of GenevaSwitzerland

Personalised recommendations

Cite paper