File:Brain morphometry image segmentation.png: Difference between revisions
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imported>Daniel Mietchen ({{Image_notes_ownwork2 |Description = Image segmentation using a priori information. In the first step, the image intensities of the 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 curves for each tissue class are fitted into the histogram to estimate the probability of a voxel b...) |
imported>Daniel Mietchen m (copyright) |
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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]]. | |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]]. | ||
|Year_created = 2009 | |Year_created = 2009 | ||
|CZ_username = [[User:Daniel Mietchen|Daniel Mietchen]] | |CZ_username = [[User:Daniel Mietchen|Daniel Mietchen]] and [[User:Christian Gaser|Christian Gaser]] | ||
|Notes = | |Notes = | ||
|Other_versions = | |Other_versions = | ||
Revision as of 07:45, 5 June 2009
Summary
| Title / Description
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Image segmentation using a priori information. In the first step, the image intensities of the 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 curves 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 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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| Author(s)
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Daniel Mietchen and Christian Gaser |
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2009 |
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