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

Roy, Pallab Kanti, Bhuiyan, Alauddin, Janke, Andrew, Desmond, Patricia M., Wong, Tien Yin, Abhayaratna, Walter P., Storey, Elsdon and Ramamohanarao, Kotagiri (2015) Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field. Computerized Medical Imaging and Graphics, 45 102-111. doi:10.1016/j.compmedimag.2015.08.005

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Author Roy, Pallab Kanti
Bhuiyan, Alauddin
Janke, Andrew
Desmond, Patricia M.
Wong, Tien Yin
Abhayaratna, Walter P.
Storey, Elsdon
Ramamohanarao, Kotagiri
Title Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field
Journal name Computerized Medical Imaging and Graphics   Check publisher's open access policy
ISSN 1879-0771
0895-6111
Publication date 2015-10-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.compmedimag.2015.08.005
Open Access Status File (Author Post-print)
Volume 45
Start page 102
End page 111
Total pages 10
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
Abstract 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.
Keyword White matter lesions (WMLs)
Magnetic resonance imaging (MRI)
Random forest (RF)
Markov Random Field (MRF)
Cerebrovascular diseases
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
Centre for Advanced Imaging Publications
 
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