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

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 d...

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Main Author:	Ong , Kok Haur
Format:	Thesis
Language:	English
Published:	2011
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/42262/1/ONG_KOK_HAUR.pdf
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id	my-usm-ep.42262
record_format	uketd_dc
spelling	my-usm-ep.422622019-04-12T05:26:33Z White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach 2011-06 Ong , Kok Haur QA75.5-76.95 Electronic computers. Computer science 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. 2011-06 Thesis http://eprints.usm.my/42262/ http://eprints.usm.my/42262/1/ONG_KOK_HAUR.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Ong , Kok Haur White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
description	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.
format	Thesis
qualification_level	Master's degree
author	Ong , Kok Haur
author_facet	Ong , Kok Haur
author_sort	Ong , Kok Haur
title	White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
title_short	White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
title_full	White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
title_fullStr	White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
title_full_unstemmed	White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
title_sort	white-matter lesion segmentation in brain mri using adaptive trimmed mean approach
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2011
url	http://eprints.usm.my/42262/1/ONG_KOK_HAUR.pdf
_version_	1747821047113056256

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

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