NucleusEditor
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NucleusEditor allows users to view the image data, and the automatically generated multivariate metadata (mainly [[Intrinsic_Features_of_Blobs|object features]]) using multiple data visualization tools that are all actively linked. This linkage is important, and allows the data to be viewed in multiple spaces simultaneously, and a cluster of objects to be selected based on any combination of operations in any of the multiple spaces. We term this method [[EVS/ALISA |Actively linked multiple spaces architecture (ALISA)]]. | NucleusEditor allows users to view the image data, and the automatically generated multivariate metadata (mainly [[Intrinsic_Features_of_Blobs|object features]]) using multiple data visualization tools that are all actively linked. This linkage is important, and allows the data to be viewed in multiple spaces simultaneously, and a cluster of objects to be selected based on any combination of operations in any of the multiple spaces. We term this method [[EVS/ALISA |Actively linked multiple spaces architecture (ALISA)]]. | ||
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| + | In addition to viewing existing results, NucleusEditor makes it possible to segment images of of cell nuclei producing features with proper cell classifications. Since automated segmentation algorithms are not perfect, there is a compelling need to develop efficient methods to identify and correct the automated segmentation errors. | ||
Revision as of 18:36, 3 July 2009
NucleusEditor allows users to view the image data, and the automatically generated multivariate metadata (mainly object features) using multiple data visualization tools that are all actively linked. This linkage is important, and allows the data to be viewed in multiple spaces simultaneously, and a cluster of objects to be selected based on any combination of operations in any of the multiple spaces. We term this method Actively linked multiple spaces architecture (ALISA).
In addition to viewing existing results, NucleusEditor makes it possible to segment images of of cell nuclei producing features with proper cell classifications. Since automated segmentation algorithms are not perfect, there is a compelling need to develop efficient methods to identify and correct the automated segmentation errors.
