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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| التنسيق: | أطروحة |
| اللغة: | English English |
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2004
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| الوصول للمادة أونلاين: | http://psasir.upm.edu.my/id/eprint/74/1/1000548980_t_FK_2004_32.pdf |
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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 |