Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm

An important area of current research is obtaining more information about brain structure and function. Brain tissue is particularly complex structure and its segmentation is an important step for studies intemporal change, detection of morphology as well as visualization in surgical planning, vo...

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Main Author:	Abdurrahim, Salem Hamed
Format:	Thesis
Language:	English English
Published:	2004
Subjects:	Magnetic resonance imaging - Case studies Watershed Brain - Abnormalities
Online Access:	http://psasir.upm.edu.my/id/eprint/74/1/1000548980_t_FK_2004_32.pdf
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spelling	my-upm-ir.742013-05-27T06:45:28Z Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm 2004-05 Abdurrahim, Salem Hamed An important area of current research is obtaining more information about brain structure and function. Brain tissue is particularly complex structure and its segmentation is an important step for studies intemporal change, detection of morphology as well as visualization in surgical planning, volume estimation of objects of interest, and more could benefit enormously from segmentation. Magnetic resonance imaging (MRI) is a noninvasive method for producing tomographic images of the human brain. Its Segmentation is problematic due to radio frequency inhomogeneity, caused by inaccuracies in the magnetic resonance scanner and by movement of the patient which produce intensity variation over the image, and that makes every segmentation method fail. The aim of this work is the development of a segmentation technique for efficient and accurate segmentation of MR brain images. The proposed technique based on the watershed algorithm, which is applied to the gradient magnitude of the MRI data. The watershed segmentation algorithm is a very powerful segmentation tool, but it also has difficulty in segmenting MR images due to noise and shading effect present. The known drawback of the watershed algorithm, over-segmentation, is strongly reduced by making the system interactive (semi-automatic), by placing markers manually in the region of interest which is the brain as well as in the background. The background markers are needed to define the external contours of the brain. The final part of the segmentation takes place once the gradient magnitudes of the MRI data are calculated and markers have been obtained from each region. Catchment’s basins originate from each of the markers, resulting in a common line of separation between brain and surrounding. The proposed segmentation technique is tested and evaluated on brain images taken from brainweb. Brainweb is maintained by the Brain Imaging Center at the Montreal Neurological Institute. The images had a combination of noise and intensity non-uniformity (INU). By making the system semi-automatic, a good segmentation result was obtained under all the conditions (different noise levels and intensity non uniformity). It is also proven that the placement of internal and external markers into regions of interest (i.e. making the system interactive) can easily cope with the over-segmentation problem of the watershed. Magnetic resonance imaging - Case studies Watershed Brain - Abnormalities 2004-05 Thesis http://psasir.upm.edu.my/id/eprint/74/ http://psasir.upm.edu.my/id/eprint/74/1/1000548980_t_FK_2004_32.pdf application/pdf en public masters Universiti Putra Malaysia Magnetic resonance imaging - Case studies Watershed Brain - Abnormalities Faculty of Engineering English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Magnetic resonance imaging - Case studies Watershed Brain - Abnormalities
spellingShingle	Magnetic resonance imaging - Case studies Watershed Brain - Abnormalities Abdurrahim, Salem Hamed Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
description	An important area of current research is obtaining more information about brain structure and function. Brain tissue is particularly complex structure and its segmentation is an important step for studies intemporal change, detection of morphology as well as visualization in surgical planning, volume estimation of objects of interest, and more could benefit enormously from segmentation. Magnetic resonance imaging (MRI) is a noninvasive method for producing tomographic images of the human brain. Its Segmentation is problematic due to radio frequency inhomogeneity, caused by inaccuracies in the magnetic resonance scanner and by movement of the patient which produce intensity variation over the image, and that makes every segmentation method fail. The aim of this work is the development of a segmentation technique for efficient and accurate segmentation of MR brain images. The proposed technique based on the watershed algorithm, which is applied to the gradient magnitude of the MRI data. The watershed segmentation algorithm is a very powerful segmentation tool, but it also has difficulty in segmenting MR images due to noise and shading effect present. The known drawback of the watershed algorithm, over-segmentation, is strongly reduced by making the system interactive (semi-automatic), by placing markers manually in the region of interest which is the brain as well as in the background. The background markers are needed to define the external contours of the brain. The final part of the segmentation takes place once the gradient magnitudes of the MRI data are calculated and markers have been obtained from each region. Catchment’s basins originate from each of the markers, resulting in a common line of separation between brain and surrounding. The proposed segmentation technique is tested and evaluated on brain images taken from brainweb. Brainweb is maintained by the Brain Imaging Center at the Montreal Neurological Institute. The images had a combination of noise and intensity non-uniformity (INU). By making the system semi-automatic, a good segmentation result was obtained under all the conditions (different noise levels and intensity non uniformity). It is also proven that the placement of internal and external markers into regions of interest (i.e. making the system interactive) can easily cope with the over-segmentation problem of the watershed.
format	Thesis
qualification_level	Master's degree
author	Abdurrahim, Salem Hamed
author_facet	Abdurrahim, Salem Hamed
author_sort	Abdurrahim, Salem Hamed
title	Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
title_short	Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
title_full	Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
title_fullStr	Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
title_full_unstemmed	Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
title_sort	segmentation of magnetic resonance brain images using watershed algorithm
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Engineering
publishDate	2004
url	http://psasir.upm.edu.my/id/eprint/74/1/1000548980_t_FK_2004_32.pdf
_version_	1747810161427218432

Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm

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