An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa

White matter hyperintensities (WMH) are associated with cognitive impairment, risk of stroke and risk of dementia and a common finding in magnetic resonance imaging (MRI) images. Early detection of WMH is important for clinical analysis on effective prevention planning by medical authorities. WMH de...

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Bibliographic Details
Main Author:	Isa, Iza Sazanita
Format:	Book Section
Language:	English
Published:	Institute of Graduate Studies, UiTM 2018
Subjects:	Cancer Soft tissue tumors
Online Access:	http://ir.uitm.edu.my/id/eprint/22218/ http://ir.uitm.edu.my/id/eprint/22218/1/ABS_IZA%20SAZANITA%20ISA%20TDRA%20VOL%2014%20IGS%2018.pdf

id	uitm-22218
recordtype	eprints
spelling	uitm-222182018-11-16T08:57:46Z http://ir.uitm.edu.my/id/eprint/22218/ An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa Isa, Iza Sazanita Cancer Soft tissue tumors White matter hyperintensities (WMH) are associated with cognitive impairment, risk of stroke and risk of dementia and a common finding in magnetic resonance imaging (MRI) images. Early detection of WMH is important for clinical analysis on effective prevention planning by medical authorities. WMH delineation on MRI images manually identified by experienced radiologists commonly uses visual score. However, the manual method is time-consuming, tedious, labour-intensive and inter-variability. Recently, research on fully automated WMH identification that aims to overcome the problems of manual delineation has attracted a lot of attention. This research proposes a method for automatic detection of WMH in white matter (WM) tissue for MRI images. A multimodal image processing which combines segmentation and enhancement procedure is proposed to process different MRI image weighted which are T2-weighted imaging (T2-WI) and fluid-attenuated inversion recovery (FLAIR). Generally, the proposed automated identification system is divided into three stages namely first, second and third stages respectively. The first stage is preprocessing procedure that combines the thresholding and filtering algorithm for pre processing the MRI images while the second stage contains two phases of main processing techniques of enhancement and segmentation. This research proposes a new enhancement technique based on the Adaptive Histogram Equalization (AHE) method. Meanwhile, the segmentation phase utilizes the two-tier segmentation to segregate brain tissues into WM, GM, CSF and WMH. A new method to initialize the clustering centers was also proposed to improve the results of segmentation. The last stage is post processing where the integration process for mapping the potential WMH obtainable from the segmentation and enhancement stage is proposed to localize the most precise WMH. In parallel, the automated performance and manual delineation for WMH identification is validated to determine the degree of similarity between both the methods. In addition, this research also proposes to classify the WMH severity based on the features of segmented WMH. In the WMH classification stage, the research considered two types of WMH features of volume and intensities to classify the severity. Finally, a software system that utilized the best of above mentioned methods is proposed to perform the automated WMH detection. Overall, the software provides good ability in identifying and classifying various loads of WMH. The software managed to achieve high capability to automatically identify WMH loads as closed as the manually delineated WMH by radiologist with more than 70% correlation. The software also showed high sensitivity on detection WMH given by TPR and PPV of 77% and 75% respectively. These findings suggest the potential use of this software as aided tool for radiologist in detecting WMH, especially to predict patients with higher risk of several chronic diseases. Institute of Graduate Studies, UiTM 2018 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/22218/1/ABS_IZA%20SAZANITA%20ISA%20TDRA%20VOL%2014%20IGS%2018.pdf Isa, Iza Sazanita (2018) An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 14 (14). Institute of Graduate Studies, UiTM, Shah Alam.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Cancer Soft tissue tumors
spellingShingle	Cancer Soft tissue tumors Isa, Iza Sazanita An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
description	White matter hyperintensities (WMH) are associated with cognitive impairment, risk of stroke and risk of dementia and a common finding in magnetic resonance imaging (MRI) images. Early detection of WMH is important for clinical analysis on effective prevention planning by medical authorities. WMH delineation on MRI images manually identified by experienced radiologists commonly uses visual score. However, the manual method is time-consuming, tedious, labour-intensive and inter-variability. Recently, research on fully automated WMH identification that aims to overcome the problems of manual delineation has attracted a lot of attention. This research proposes a method for automatic detection of WMH in white matter (WM) tissue for MRI images. A multimodal image processing which combines segmentation and enhancement procedure is proposed to process different MRI image weighted which are T2-weighted imaging (T2-WI) and fluid-attenuated inversion recovery (FLAIR). Generally, the proposed automated identification system is divided into three stages namely first, second and third stages respectively. The first stage is preprocessing procedure that combines the thresholding and filtering algorithm for pre processing the MRI images while the second stage contains two phases of main processing techniques of enhancement and segmentation. This research proposes a new enhancement technique based on the Adaptive Histogram Equalization (AHE) method. Meanwhile, the segmentation phase utilizes the two-tier segmentation to segregate brain tissues into WM, GM, CSF and WMH. A new method to initialize the clustering centers was also proposed to improve the results of segmentation. The last stage is post processing where the integration process for mapping the potential WMH obtainable from the segmentation and enhancement stage is proposed to localize the most precise WMH. In parallel, the automated performance and manual delineation for WMH identification is validated to determine the degree of similarity between both the methods. In addition, this research also proposes to classify the WMH severity based on the features of segmented WMH. In the WMH classification stage, the research considered two types of WMH features of volume and intensities to classify the severity. Finally, a software system that utilized the best of above mentioned methods is proposed to perform the automated WMH detection. Overall, the software provides good ability in identifying and classifying various loads of WMH. The software managed to achieve high capability to automatically identify WMH loads as closed as the manually delineated WMH by radiologist with more than 70% correlation. The software also showed high sensitivity on detection WMH given by TPR and PPV of 77% and 75% respectively. These findings suggest the potential use of this software as aided tool for radiologist in detecting WMH, especially to predict patients with higher risk of several chronic diseases.
format	Book Section
author	Isa, Iza Sazanita
author_facet	Isa, Iza Sazanita
author_sort	Isa, Iza Sazanita
title	An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
title_short	An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
title_full	An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
title_fullStr	An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
title_full_unstemmed	An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa
title_sort	automated multimodal white matter hyperintensities identification in mri brain images using image processing / iza sazanita isa
publisher	Institute of Graduate Studies, UiTM
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/22218/ http://ir.uitm.edu.my/id/eprint/22218/1/ABS_IZA%20SAZANITA%20ISA%20TDRA%20VOL%2014%20IGS%2018.pdf
first_indexed	2023-09-18T23:08:12Z
last_indexed	2023-09-18T23:08:12Z
_version_	1777418624894500864

An automated multimodal white matter hyperintensities identification in MRI brain images using image processing / Iza Sazanita Isa

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