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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Roy, PK; Bhuiyan, A; Janke, A; Desmond, PM; Wong, TY; Abhayaratna, WP; Storey, E; Ramamohanarao, K

Published Date

  • October 2015

Published In

Volume / Issue

  • 45 /

Start / End Page

  • 102 - 111

PubMed ID

  • 26398564

Pubmed Central ID

  • 26398564

Electronic International Standard Serial Number (EISSN)

  • 1879-0771

Digital Object Identifier (DOI)

  • 10.1016/j.compmedimag.2015.08.005

Language

  • eng

Conference Location

  • United States