File:Brain morphometry image segmentation.png
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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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| Date created
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2009 |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 19:53, 11 March 2022 | | 2,937 × 1,776 (1.18 MB) | Maintenance script (talk | contribs) | == Summary == Importing file |
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