imported>Caesar Schinas |
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| == Summary == | | == Summary == |
| {{Image_notes_ownwork2
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| |Description = Image segmentation using [[a priori]] information. In the first step, the image intensities of the [[Magnetic resonance imaging|T1 image]] (upper left) are used to plot their frequencies in a [[histogram]]. Several peaks – corresponding to different image intensities of the tissue classes – can be differentiated. In the next step, [[Gaussian curve]]s for each tissue class are fitted into the histogram to estimate the probability of a [[voxel]] belonging to that tissue class (bottom left). A map for [[gray matter]] is shown (upper right) with the estimated probability for two selected locations (red circles). Based solely on a similar image intensity, the [[cerebral]] and the [[skull|extracranial]] spot exhibit a similar probability for belonging to gray matter. This can be corrected by combining the image intensity-based information with prior information (below), e.g. using a [[Bayesian approach]].
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| |Year_created = 2009
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| |CZ_username = [[User:Daniel Mietchen|Daniel Mietchen]] and [[User:Christian Gaser|Christian Gaser]]
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| |Notes =
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| |Other_versions =
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| == Licensing/Copyright status ==
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| {{CC|by-sa|3.0}}
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Latest revision as of 18:53, 11 March 2022
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