White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach

dc.contributor.authorOng, Kok Haur
dc.date.accessioned2018-08-13T08:21:39Z
dc.date.available2018-08-13T08:21:39Z
dc.date.issued2011-06
dc.description.abstractWhite 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=3:94x10􀀀6) 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/6286
dc.language.isoenen_US
dc.publisherUniversiti Sains Malaysiaen_US
dc.subjectWhite-matter lesion segmentation in brainen_US
dc.subjectusing adaptive trimmed mean approachen_US
dc.titleWhite-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approachen_US
dc.typeThesisen_US
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