Hi Nick, Brian and I have been discussing the new point set metrics and how to use them with labels. We're thinking we'll want to have single point sets containing multiple labels, and the metrics will evaluate using the labels. Presumably we'll store the label information in the point set data.
One need is to be able to perform neighbor searches constrained by label, and probabilistic searches e.g. for the expectation-based and jensen point set metrics. Currently the PointsLocator is used within the point set metric to find N nearest neighbors, which creates a k-d tree representation of the point set during pre-processing. Do we add per-label search directly to the point sets, presumably by adding a new k-d tree type? Perhaps creating one that does probabilistic neighbor searching as well? Or do we pre-process the user-supplied point sets into individual point sets for each label, and then just use the exciting point locator methods on those? Do we create a separate LabeledPointSet class that knows how to do these things, and require that the point set metrics take this type? What do you think? -M _______________________________________________ Powered by www.kitware.com Visit other Kitware open-source projects at http://www.kitware.com/opensource/opensource.html Kitware offers ITK Training Courses, for more information visit: http://kitware.com/products/protraining.php Please keep messages on-topic and check the ITK FAQ at: http://www.itk.org/Wiki/ITK_FAQ Follow this link to subscribe/unsubscribe: http://www.itk.org/mailman/listinfo/insight-developers
