Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution

Magnetic Resonance Imaging (MRI) is extensively used in the study of brain. Segmentation of MR brain images is necessary for a number of clinical investigations of various complexity, change detection, cortical labeling, and visualization in surgical planning. The volume of enhancing lesions, fol...

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Main Author:	Salih Al-Badri, Qussay Abbas
Format:	Thesis
Language:	English English
Published:	2006
Subjects:	Brain - Tumors - Magnetic resonance imaging - Case studies
Online Access:	http://psasir.upm.edu.my/id/eprint/6109/1/Fk_2006_19.pdf
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id	my-upm-ir.6109
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spelling	my-upm-ir.61092023-10-09T03:12:13Z Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution 2006-01 Salih Al-Badri, Qussay Abbas Magnetic Resonance Imaging (MRI) is extensively used in the study of brain. Segmentation of MR brain images is necessary for a number of clinical investigations of various complexity, change detection, cortical labeling, and visualization in surgical planning. The volume of enhancing lesions, following the administration of paramagnetic contrast agent is an important indicator of pathology in multiple sclerosis (MS). Manual estimation of enhancing lesion volumes introduces significant errors, and operator bias, besides being time consuming and subjective. Therefore, there is a need for automatic identification and estimation of volumes of the present MS lesions specially by dealing with a large number of images that are typically acquired in multi-center clinical trials. In the developed techniques, 150 T1- and T2-weighted spin echo images were taken from the routine scans of Kuala Lumpur General Hospital.Multiple sclerosis lesions visualized by morphological MRI are classified through a feature map technique on T1 weighted MRI tissue. Gray level morphology methods are used to make tissue types in the images more homogenous and minimize difficulties with connections to outside tissue. A method for hzzy connectedness and combinations of the different segmentation techniques were experimented. A gain-based correction method; probability density function model are used to cluster white and gray matters, cerebrospinal fluid, and meninges. Results of segmentation have been validated by a group of neuro-radiologists. 3D visualization has been implemented for the segmented regions as well as brain lesion. The visualization of the segmented structures uses a combination of volume rendering and surface rendering. The mutual information algorithms used in this work has been developed and experimented in the system and has proven to yield more accurate and stable results than other algorithms. Currently testing the validation of the proposed segmentation in a validation study that compares resulting MS lesion as well as gray and white matter tissue structures with Neural Network expert segmentation system. The proposed method versus Neural Network rater validation showed an average validation score of overlap ratio of >85% for gray and white matters tissue segmentation and for MS lesion the rater validation showed an average overlap ratio of > 87%. Brain - Tumors - Magnetic resonance imaging - Case studies 2006-01 Thesis http://psasir.upm.edu.my/id/eprint/6109/ http://psasir.upm.edu.my/id/eprint/6109/1/Fk_2006_19.pdf text en public doctoral Universiti Putra Malaysia Brain - Tumors - Magnetic resonance imaging - Case studies Engineering Ramli, Abdul Rahman English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
advisor	Ramli, Abdul Rahman
topic	Brain - Tumors - Magnetic resonance imaging - Case studies
spellingShingle	Brain - Tumors - Magnetic resonance imaging - Case studies Salih Al-Badri, Qussay Abbas Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
description	Magnetic Resonance Imaging (MRI) is extensively used in the study of brain. Segmentation of MR brain images is necessary for a number of clinical investigations of various complexity, change detection, cortical labeling, and visualization in surgical planning. The volume of enhancing lesions, following the administration of paramagnetic contrast agent is an important indicator of pathology in multiple sclerosis (MS). Manual estimation of enhancing lesion volumes introduces significant errors, and operator bias, besides being time consuming and subjective. Therefore, there is a need for automatic identification and estimation of volumes of the present MS lesions specially by dealing with a large number of images that are typically acquired in multi-center clinical trials. In the developed techniques, 150 T1- and T2-weighted spin echo images were taken from the routine scans of Kuala Lumpur General Hospital.Multiple sclerosis lesions visualized by morphological MRI are classified through a feature map technique on T1 weighted MRI tissue. Gray level morphology methods are used to make tissue types in the images more homogenous and minimize difficulties with connections to outside tissue. A method for hzzy connectedness and combinations of the different segmentation techniques were experimented. A gain-based correction method; probability density function model are used to cluster white and gray matters, cerebrospinal fluid, and meninges. Results of segmentation have been validated by a group of neuro-radiologists. 3D visualization has been implemented for the segmented regions as well as brain lesion. The visualization of the segmented structures uses a combination of volume rendering and surface rendering. The mutual information algorithms used in this work has been developed and experimented in the system and has proven to yield more accurate and stable results than other algorithms. Currently testing the validation of the proposed segmentation in a validation study that compares resulting MS lesion as well as gray and white matter tissue structures with Neural Network expert segmentation system. The proposed method versus Neural Network rater validation showed an average validation score of overlap ratio of >85% for gray and white matters tissue segmentation and for MS lesion the rater validation showed an average overlap ratio of > 87%.
format	Thesis
qualification_level	Doctorate
author	Salih Al-Badri, Qussay Abbas
author_facet	Salih Al-Badri, Qussay Abbas
author_sort	Salih Al-Badri, Qussay Abbas
title	Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
title_short	Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
title_full	Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
title_fullStr	Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
title_full_unstemmed	Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution
title_sort	tumor extraction for brain magnetic resonance imaging using modified gaussian distribution
granting_institution	Universiti Putra Malaysia
granting_department	Engineering
publishDate	2006
url	http://psasir.upm.edu.my/id/eprint/6109/1/Fk_2006_19.pdf
_version_	1783725681875615744

Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution

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