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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
http://psasir.upm.edu.my/id/eprint/6109/
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spelling my.upm.eprints.61092023-10-09T03:12:13Z http://psasir.upm.edu.my/id/eprint/6109/ Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution 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%. 2006-01 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/6109/1/Fk_2006_19.pdf Salih Al-Badri, Qussay Abbas (2006) Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution. Doctoral thesis, Universiti Putra Malaysia. Brain - Tumors - Magnetic resonance imaging - Case studies English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
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
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
publishDate 2006
url http://psasir.upm.edu.my/id/eprint/6109/1/Fk_2006_19.pdf
http://psasir.upm.edu.my/id/eprint/6109/
_version_ 1781706599834845184
score 13.211869

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