2012 Winter Project Week:PatchBased - Revision history

Wachinge: /* Investigators */

2012-01-13T00:17:54Z

Investigators

2012-01-13T00:16:22Z

2012-01-05T09:21:59Z

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 <h3>Approach, Plan</h3>
 In order to evaluate the discriminative power of patches, we perform manifold learning to project points from high-dimensional patch space to lower dimensions. Having the low-dimensional embedding, we can assess if patches that correspond to the same label arrange in clusters.
-One challenge is the very large number of patches in an image or a group of images, posing problems to standard spectral methods for dimensionality reduction. Consequently we investigate the application of approaches to deal with a large number of samples, such as the Nyström method.
+One challenge is the very large number of patches in an image or a group of images, posing problems to standard spectral methods for dimensionality reduction. Consequently we investigate the application of approaches to deal with a large number of samples.
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 <h3>Progress</h3>
+* Discussed data and motivation with fellow project week participants
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 Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
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 <h3>Objective</h3>
 We will investigate the applicability of a patch-based approach in the context of label fusion.
-This could, for instance, be applied to the automatic segmentation of organs at risk in the head and neck data.
+This is interesting in scenarios, where the subjects show a high variability and consequently making the calculation of the deformation fields between them challenging.
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 <h3>Approach, Plan</h3>
+In order to evaluate the discriminative power of patches, we perform manifold learning to project points from high-dimensional patch space to lower dimensions. Having the low-dimensional embedding, we can assess if patches that correspond to the same label arrange in clusters.
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