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

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

Highlights • An automatic method for white matter lesion segmentation (WML). • A novel filter is used to enhance the contrast of WML. • A robust Markov Random Field is used to remove false positive WMLs. • Evaluated with respect to the grading of two expert graders. • Compared with state of the art...

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Veröffentlicht in:Computerized medical imaging and graphics 2015-10, Vol.45, p.102-111
Hauptverfasser: Roy, Pallab Kanti, Bhuiyan, Alauddin, Janke, Andrew, Desmond, Patricia M, Wong, Tien Yin, Abhayaratna, Walter P, Storey, Elsdon, Ramamohanarao, Kotagiri
Format: Artikel
Sprache:eng
Schlagworte:
Brain - pathology
Cerebrovascular diseases
Computer Simulation
Contrast Media
Data Interpretation, Statistical
Diffusion Tensor Imaging - methods
Humans
Image Interpretation, Computer-Assisted - methods
Internal Medicine
Leukoencephalopathies - pathology
Machine Learning
Magnetic resonance imaging (MRI)
Markov Chains
Markov Random Field (MRF)
Models, Statistical
Observer Variation
Other
Pattern Recognition, Automated - methods
Random forest (RF)
Reproducibility of Results
Sensitivity and Specificity
Subtraction Technique
White Matter - pathology
White matter lesions (WMLs)
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Zusammenfassung:Highlights • An automatic method for white matter lesion segmentation (WML). • A novel filter is used to enhance the contrast of WML. • A robust Markov Random Field is used to remove false positive WMLs. • Evaluated with respect to the grading of two expert graders. • Compared with state of the art methods in two datasets.
ISSN:0895-6111
1879-0771
DOI:10.1016/j.compmedimag.2015.08.005