skip to main content
10.1145/3378904.3378927acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbdetConference Proceedingsconference-collections
research-article

Detection of Focal Cortical Dysplasia lesions in MR images

Authors Info & Claims
Published:09 April 2020Publication History

ABSTRACT

Focal cortical dysplasia (FCD) is the most common factor leading to intractable epilepsy. It is helpful for doctors to automatically detect the FCD lesion before the operation. In this study, two methods to detect and locate the lesion are proposed. The first method is based on the symmetrical characteristics of the brain image, and can approximately detect abnormal areas caused by FCD in Magnetic Resonance (MR) images. The second method involves detecting local highlighted areas in MR images based on the expectation-maximization (EM) algorithm. The two methods were applied to 15 specific MR images of 9 epileptic patients. The recognition accuracy of the symmetrical feature algorithm and EM algorithm was 80 % (12/15) and 100% (15/15), respectively, and the detection accuracy of the combination of two algorithms is 80%. The symmetrical feature algorithm can be applied to any of the axial or coronal MR images of the modality, and the EM algorithm is suitable for detecting the local hyperintense of the MR image. The two methods were able to detect lesion areas in MR images and achieve desirable results.

References

  1. Taylor D C, Falconer M A, Bruton C J, et al. Focal dysplasia of the cerebral cortex in epilepsy[J]. Journal of Neurology Neurosurgery & Psychiatry, 1971, 34(4):369--387.Google ScholarGoogle ScholarCross RefCross Ref
  2. Kabat J, Król P. Focal cortical dysplasia - review[J]. Polish Journal of Radiology, 2012, 77(2):35--43.Google ScholarGoogle ScholarCross RefCross Ref
  3. Tassi L, Colombo N, Garbelli R, et al. Focal cortical dysplasia: neuropathological subtypes, EEG, neuroimaging and surgical outcome[J]. Brain A Journal of Neurology, 2002, 125(Pt 8):1719--1732.Google ScholarGoogle ScholarCross RefCross Ref
  4. Palmini A, Najm I, Avanzini G, et al. Terminology and classification of the cortical dysplasias[J]. Neurology, 2004, 62(6 Suppl 3):S2-8.Google ScholarGoogle ScholarCross RefCross Ref
  5. Kassubek J, Huppertz H J, Spreer J, et al. Detection and localization of focal cortical dysplasia by voxel-based 3-D MRI analysis.[J]. Epilepsia, 2002, 43(6):596--602.Google ScholarGoogle ScholarCross RefCross Ref
  6. Huppertz H J, Grimm C, Fauser S, et al. Enhanced visualization of blurred gray-white matter junctions in focal cortical dysplasia by voxel-based 3D MRI analysis.[J]. Epilepsy Research, 2005, 67(1-2):35.Google ScholarGoogle ScholarCross RefCross Ref
  7. Colliot O, Bernasconi N, Khalili N, et al. Individual voxel-based analysis of gray matter in focal cortical dysplasia[J]. Neuroimage, 2006, 29(1):162--171.Google ScholarGoogle ScholarCross RefCross Ref
  8. Besson, Pierre, Bernasconi, et al. Surface-Based Texture and Morphological Analysis Detects Subtle Cortical Dysplasia[M]// Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2008. Springer Berlin Heidelberg, 2008:645--52.Google ScholarGoogle Scholar
  9. Bernasconi A, Antel S B, Collins D L, et al. Texture analysis and morphological processing of magnetic resonance imaging assist detection of focal cortical dysplasia in extra-temporal partial epilepsy[J]. Annals of Neurology, 2001, 49(6):770--775.Google ScholarGoogle ScholarCross RefCross Ref
  10. Antel S B, Collins D L, Bernasconi N, et al. Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis[J]. Neuroimage, 2003, 19(4):1748--1759.Google ScholarGoogle ScholarCross RefCross Ref
  11. Antel S B, Bernasconi A, Bernasconi N, et al. Computational models of MRI characteristics of focal cortical dysplasia improve lesion detection[J]. Neuroimage, 2002, 17(4):1755--1760.Google ScholarGoogle ScholarCross RefCross Ref
  12. Barkovich AJ, Kuzniecky RI, Jackson GD, Guerrini R, Dobyns WB. Classification system for malformations of cortical development: Update. Neurology.2001; 57(12):2168--78.Google ScholarGoogle Scholar
  13. http://radiopaedia.org/cases/focal-cortical-dysplasia-type-iib: Case courtesy of A.Prof Frank Gaillard, Radiopaedia.org, rID: 5561Google ScholarGoogle Scholar
  14. http://radiopaedia.org/cases/focal-cortical-dysplasia-5: Case courtesy of Dr Ahmed Abd Rabou, Radiopaedia.org, rID: 39848Google ScholarGoogle Scholar
  15. Chan T F, Vese L A. Active contours without edges[J]. IEEE Press, 2001, 10(2):266--277.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Khotanlou H, Colliot O, Bloch I. Automatic Brain Tumor Segmentation Using Symmetry Analysis and Deformable Models[M]// Advances In Pattern Recognition. 2014:198--202.Google ScholarGoogle Scholar
  17. Khotanlou H, Colliot O, Atif J, et al. 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models[J]. Fuzzy Sets & Systems, 2009, 160(10):1457--1473.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Dvorak P, Kropatsch W, Bartusek K. Automatic detection of brain tumors in MR images[C]. International Conference on Telecommunications and Signal Processing. IEEE, 2013:577--580.Google ScholarGoogle Scholar
  19. Saha B N, Ray N, Greiner R, et al. Quick detection of brain tumors and edemas: A bounding box method using symmetry[J]. Comput Med Imaging Graph, 2012, 36(2):95--107.Google ScholarGoogle ScholarCross RefCross Ref
  20. DEMPSTER, A. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society, 1977, 39(1):1--38.Google ScholarGoogle Scholar

Index Terms

  1. Detection of Focal Cortical Dysplasia lesions in MR images

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      BDET 2020: Proceedings of the 2020 2nd International Conference on Big Data Engineering and Technology
      January 2020
      126 pages
      ISBN:9781450376839
      DOI:10.1145/3378904

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 9 April 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited
    • Article Metrics

      • Downloads (Last 12 months)5
      • Downloads (Last 6 weeks)0

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader