Automatic white matter lesions detection and segmentation of brain magnetic resonance images

White matter lesions (WML) are frequently associated with neuronal degeneration in ageing and can be an important indicator of stroke, multiple sclerosis, dementia and other brain-related disorders. WML can be readily detected on Magnetic Resonance Imaging (MRI), but manual delineation of lesions by...

Full description

Saved in:

Bibliographic Details
Main Author:	Ong, Kok Haur
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/96214/1/OngKokHaurPSC2019.pdf.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.96214
record_format	uketd_dc
spelling	my-utm-ep.962142022-07-05T03:15:37Z Automatic white matter lesions detection and segmentation of brain magnetic resonance images 2019 Ong, Kok Haur QA75 Electronic computers. Computer science White matter lesions (WML) are frequently associated with neuronal degeneration in ageing and can be an important indicator of stroke, multiple sclerosis, dementia and other brain-related disorders. WML can be readily detected on Magnetic Resonance Imaging (MRI), but manual delineation of lesions by neuroradiologists is a time consuming and laborious task. Furthermore, MRI intensity scales are not standardised and do not have tissue-specific interpretation, leading to WML quantification inaccuracies and difficulties in interpreting their pathological relevance. Numerous studies have shown tremendous advances in WML segmentation, but flow artefact, image noise, incomplete skull stripping and inaccurate WML classification continue to yield False Positives (FP) that have limited the reliability and clinical utility of these approaches. The present study proposed a new MRI intensity standardisation and clustered texture feature method based on the K-means clustering algorithm. Enhanced clustered texture features and histogram features were constructed based on the proposed standardisation method to significantly reduce FP through a Random Forest algorithm. Subsequently, a local outlier identification method further refined the boundary of WML for the final segmentation. The method was validated with a test set of 32 scans (279 images), with a significant correlation coefficient (R=0.99574, p-value < 0.001) between the proposed method and manual delineation by a neuroradiologist. Furthermore, comparison against three state-of-the-art methods for the 32 scans demonstrated that the proposed method outperformed five of seven well-known evaluation metrics. This improved specificity in WML segmentation may thus improve the quantification of clinical WML burden to assess for correlations between WML load and distribution with neurodenegerative disease. 2019 Thesis http://eprints.utm.my/id/eprint/96214/ http://eprints.utm.my/id/eprint/96214/1/OngKokHaurPSC2019.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143294 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ong, Kok Haur Automatic white matter lesions detection and segmentation of brain magnetic resonance images
description	White matter lesions (WML) are frequently associated with neuronal degeneration in ageing and can be an important indicator of stroke, multiple sclerosis, dementia and other brain-related disorders. WML can be readily detected on Magnetic Resonance Imaging (MRI), but manual delineation of lesions by neuroradiologists is a time consuming and laborious task. Furthermore, MRI intensity scales are not standardised and do not have tissue-specific interpretation, leading to WML quantification inaccuracies and difficulties in interpreting their pathological relevance. Numerous studies have shown tremendous advances in WML segmentation, but flow artefact, image noise, incomplete skull stripping and inaccurate WML classification continue to yield False Positives (FP) that have limited the reliability and clinical utility of these approaches. The present study proposed a new MRI intensity standardisation and clustered texture feature method based on the K-means clustering algorithm. Enhanced clustered texture features and histogram features were constructed based on the proposed standardisation method to significantly reduce FP through a Random Forest algorithm. Subsequently, a local outlier identification method further refined the boundary of WML for the final segmentation. The method was validated with a test set of 32 scans (279 images), with a significant correlation coefficient (R=0.99574, p-value < 0.001) between the proposed method and manual delineation by a neuroradiologist. Furthermore, comparison against three state-of-the-art methods for the 32 scans demonstrated that the proposed method outperformed five of seven well-known evaluation metrics. This improved specificity in WML segmentation may thus improve the quantification of clinical WML burden to assess for correlations between WML load and distribution with neurodenegerative disease.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ong, Kok Haur
author_facet	Ong, Kok Haur
author_sort	Ong, Kok Haur
title	Automatic white matter lesions detection and segmentation of brain magnetic resonance images
title_short	Automatic white matter lesions detection and segmentation of brain magnetic resonance images
title_full	Automatic white matter lesions detection and segmentation of brain magnetic resonance images
title_fullStr	Automatic white matter lesions detection and segmentation of brain magnetic resonance images
title_full_unstemmed	Automatic white matter lesions detection and segmentation of brain magnetic resonance images
title_sort	automatic white matter lesions detection and segmentation of brain magnetic resonance images
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2019
url	http://eprints.utm.my/id/eprint/96214/1/OngKokHaurPSC2019.pdf.pdf
_version_	1747818648753405952

Automatic white matter lesions detection and segmentation of brain magnetic resonance images

Similar Items