Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm
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An important area of current research is obtaining more information about
\nbrain structure and function. Brain tissue is particularly complex structure and
\nits segmentation is an important step for studies intemporal change, detection
\nof morphology as well as visualization in surgical planning, volume estimation
\nof objects of interest, and more could benefit enormously from segmentation.
\nMagnetic resonance imaging (MRI) is a noninvasive method for producing
\ntomographic images of the human brain. Its Segmentation is problematic due to
\nradio frequency inhomogeneity, caused by inaccuracies in the magnetic
\nresonance scanner and by movement of the patient which produce intensity
\nvariation over the image, and that makes every segmentation method fail.
\nThe aim of this work is the development of a segmentation technique for
\nefficient and accurate segmentation of MR brain images. The proposed
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\ntechnique based on the watershed algorithm, which is applied to the gradient
\nmagnitude of the MRI data. The watershed segmentation algorithm is a very
\npowerful segmentation tool, but it also has difficulty in segmenting MR images
\ndue to noise and shading effect present. The known drawback of the watershed
\nalgorithm, over-segmentation, is strongly reduced by making the system
\ninteractive (semi-automatic), by placing markers manually in the region of
\ninterest which is the brain as well as in the background. The background
\nmarkers are needed to define the external contours of the brain. The final part
\nof the segmentation takes place once the gradient magnitudes of the MRI data
\nare calculated and markers have been obtained from each region. Catchment’s
\nbasins originate from each of the markers, resulting in a common line of
\nseparation between brain and surrounding.
\nThe proposed segmentation technique is tested and evaluated on brain images
\ntaken from brainweb. Brainweb is maintained by the Brain Imaging Center at
\nthe Montreal Neurological Institute. The images had a combination of noise and
\nintensity non-uniformity (INU). By making the system semi-automatic, a good
\nsegmentation result was obtained under all the conditions (different noise
\nlevels and intensity non uniformity). It is also proven that the placement of
\ninternal and external markers into regions of interest (i.e. making the system
\ninteractive) can easily cope with the over-segmentation problem of the
\nwatershed.

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