White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
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Date
2011-06
Authors
Ong, Kok Haur
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
White Matter (WM) lesions are diffuse white matter abnormalities, that appear as hyperintense
(bright) regions in cranial Magnetic Resonance Imaging (MRI). WM lesions are often
observed in older population and are important indicators of stroke, multiple sclerosis, dementia
and other brain-related disorders. Manual detection of WM lesions is laborious and the
currently adopted visual scoring approaches for lesion grading is very subjective. In this thesis,
a new approach for automated WM Lesions Segmentation is presented. In the proposed
approach, 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
technique. Outliers are detected using a novel adaptive Trimmed Mean and Box-Whisker
Plots. In addition, the approach includes pre and post-processing steps to reduce False Positives
attributed to MRI artefacts commonly observed in FLAIR sequences. The proposed
approach is validated using the cranial MRI sequences of 38 subjects. A significant correlation
(R=0:8506;P=3:94x106) is observed between automated approach and manual visual scoring
(Age-related White Matter Changes Scale). The accuracy of the proposed approach was
further validated by comparing the lesion volumes computes using the automated approach and
lesions manually segmented by an expert radiologist. The proposed approach is also compared
against leading lesion segmentation algorithms using a benchmark dataset.
Description
Keywords
White-matter lesion segmentation in brain , using adaptive trimmed mean approach