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Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.

Publication ,  Journal Article
Roy, PK; Bhuiyan, A; Janke, A; Desmond, PM; Wong, TY; Abhayaratna, WP; Storey, E; Ramamohanarao, K
Published in: Comput Med Imaging Graph
October 2015

White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.

Duke Scholars

Published In

Comput Med Imaging Graph

DOI

EISSN

1879-0771

Publication Date

October 2015

Volume

45

Start / End Page

102 / 111

Location

United States

Related Subject Headings

  • White Matter
  • Subtraction Technique
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Observer Variation
  • Nuclear Medicine & Medical Imaging
  • Models, Statistical
  • Markov Chains
  • Machine Learning
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Roy, P. K., Bhuiyan, A., Janke, A., Desmond, P. M., Wong, T. Y., Abhayaratna, W. P., … Ramamohanarao, K. (2015). Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. Comput Med Imaging Graph, 45, 102–111. https://doi.org/10.1016/j.compmedimag.2015.08.005
Roy, Pallab Kanti, Alauddin Bhuiyan, Andrew Janke, Patricia M. Desmond, Tien Yin Wong, Walter P. Abhayaratna, Elsdon Storey, and Kotagiri Ramamohanarao. “Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.Comput Med Imaging Graph 45 (October 2015): 102–11. https://doi.org/10.1016/j.compmedimag.2015.08.005.
Roy PK, Bhuiyan A, Janke A, Desmond PM, Wong TY, Abhayaratna WP, et al. Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. Comput Med Imaging Graph. 2015 Oct;45:102–11.
Roy, Pallab Kanti, et al. “Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field.Comput Med Imaging Graph, vol. 45, Oct. 2015, pp. 102–11. Pubmed, doi:10.1016/j.compmedimag.2015.08.005.
Roy PK, Bhuiyan A, Janke A, Desmond PM, Wong TY, Abhayaratna WP, Storey E, Ramamohanarao K. Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. Comput Med Imaging Graph. 2015 Oct;45:102–111.
Journal cover image

Published In

Comput Med Imaging Graph

DOI

EISSN

1879-0771

Publication Date

October 2015

Volume

45

Start / End Page

102 / 111

Location

United States

Related Subject Headings

  • White Matter
  • Subtraction Technique
  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Observer Variation
  • Nuclear Medicine & Medical Imaging
  • Models, Statistical
  • Markov Chains
  • Machine Learning