Advertisement

An Enhanced Sequential Search Feature Selection Based on mRMR to Support FCD Localization

  • J. Castañeda-GonzalezEmail author
  • A. Alvarez-Meza
  • A. Orozco-Gutierrez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

One of the most common abnormalities that create a disorder in brain activity is the Focal Cortical Dysplasia (FCD), which can cause pharmacoresistant epilepsy. Patients with this kind of pathology can be treated surgically to remove the lesioned zone of the brain. However, the location of these lesions depends on the specialist expertise. Then, suitable support regarding the FCD analysis is required to minimize the localization subjectivity, primarily, for imbalance scenarios, e.g., few pathological regions are provided. In this work, we propose a new image processing approach to support FCD localization using a minimal redundancy maximal relevance-based feature selection stage that relies on a mutual information cost function to deal with imbalance problems. Then, our proposal finds a feature space through sequential searching aiming to highlight significant relationships between FCD labels and structural-based parameters from magnetic resonance brain images. Achieved results show a more significant improvement in terms of classifications statistics compared to state-of-the-art works.

Keywords

Image processing Imbalance classification Feature selection 

Notes

Acknowledgments

Under grants provided by the project “Desarrollo de un sistema de apoyo al diagnóstico no invasivo de pacientes con epilepsia farmacoresistente asociada a displasias corticales cerebrales: método costo efectivo basado en procesamiento de imágenes de resonancia magnética” with code 111074455778 funded by COLCIENCIAS. J. Castañeda is partially funded by “Metodología para la segmentación automática de la corteza cerebral sobre imágenes MRI con base en características volumétricas usadas en técnicas de renderizado tridimensional por funciones de transferencia” by the Vicerrectoria de Investigación and the Maestría en ingeniría eléctrica program, both from the Universidad Tecnológica de Pereira.

References

  1. 1.
    Adler, S., Wagstyl, K., Gunny, R., et al.: Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy. NeuroImage Clin. 14, 18–27 (2017)CrossRefGoogle Scholar
  2. 2.
    Ahmed, B., et al.: Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia. Epilepsy Behav. 48, 21–28 (2015)CrossRefGoogle Scholar
  3. 3.
    Elkan, C.: The foundations of cost-sensitive learning. In: International Joint Conference on Artificial Intelligence, vol. 17, pp. 973–978. Lawrence Erlbaum Associates Ltd. (2001)Google Scholar
  4. 4.
    He, H.: Imbalanced learning. In: Self-Adaptive Systems for Machine Intelligence, pp. 44–107 (2013)Google Scholar
  5. 5.
    Hong, S.-J., Kim, H., Schrader, D., Bernasconi, N., Bernhardt, B.C., Bernasconi, A.: Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology 83(1), 48–55 (2014)CrossRefGoogle Scholar
  6. 6.
    Hoyos-Osorio, K., Álvarez, A.M., Orozco, Á.A., Rios, J.I., Daza-Santacoloma, G.: Clustering-based undersampling to support automatic detection of focal cortical dysplasias. In: Mendoza, M., Velastín, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 298–305. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75193-1_36CrossRefGoogle Scholar
  7. 7.
    Najm, I.M., Tassi, L., Sarnat, H.B., Holthausen, H., Russo, G.L.: Epilepsies associated with Focal Cortical Dysplasias (FCDs). Acta Neuropathol. 128(1), 5–19 (2014)CrossRefGoogle Scholar
  8. 8.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Analy. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  9. 9.
    Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Rusboost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(1), 185–197 (2010)CrossRefGoogle Scholar
  10. 10.
    Wiwattanadittakul, N., et al.: Location, size of focal cortical dysplasia, and age of seizure onset in children who underwent epilepsy surgery. In: EPILEPSIA, NJ, USA, vol. 58 (2017)Google Scholar
  11. 11.
    Naghibi, T., Hoffmann, S., Pfister, B.: A semidefinite programming based search strategy for feature selection with mutual information measure. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1529–1541 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • J. Castañeda-Gonzalez
    • 1
    Email author
  • A. Alvarez-Meza
    • 1
  • A. Orozco-Gutierrez
    • 1
  1. 1.Automatics Research GroupUniversidad Tecnológica de PereiraPereiraColombia

Personalised recommendations