期刊
MAGNETIC RESONANCE IMAGING
卷 30, 期 6, 页码 807-823出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2012.01.007
关键词
MRI; Outlier; White matter lesions; White matter hyperintensities; Leukoaraiosis; Adaptive trimmed mean algorithm; Box-whisker plot
资金
- Universiti Sains Malaysia's Research University Grant Delineation and 3D Visualization of Tumor and Risk Structures (DVTRS) [1001/PKOMP/817001]
White matter (WM) lesions are diffuse WM abnormalities that appear as hyperintense (bright) regions in cranial magnetic resonance imaging (MRI). WM lesions are often observed in older populations and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. In this paper, a new automated method for WM lesions segmentation is presented. In the proposed method, the presence of WM lesions is detected as outliers in the intensity distribution of the fluid-attenuated inversion recovery (FLAIR) MR images using an adaptive outlier detection approach. Outliers are detected using a novel adaptive trimmed mean algorithm and box-whisker plot. In addition, pre- and postprocessing steps are implemented to reduce false positives attributed to MRI artifacts commonly observed in FLAIR sequences. The approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation (R=0.9641, P value=3.12x10(-3)) is observed between the automated approach and manual segmentation by radiologist. The accuracy of the proposed approach was further validated by comparing the lesion volumes computed using the automated approach and lesions manually segmented by an expert radiologist. Finally, the proposed approach is compared against leading lesion segmentation algorithms using a benchmark dataset. Crown Copyright (C) 2012 Published by Elsevier Inc. All rights reserved.
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