Clustered Batch Trainable Weka Segmentation

A plugin to parallelize the application of a Trainable Weka Segmentation classifier to a single image or a stack of images. The given image is split into several smaller blocks which are classified by remote nodes, then reassembled into the probability map image, one map per class.

* Designed with serial section electron microscopy in mind
* Works only for the 2D segmentation case
* The probability maps for each class are saved on disk as tif files, and opened in a virtual stack, one stack per class.
* Added function setTrainheader(Instances newHeader) to WekaSegmentation.java

To Use (With an image, image stack, or virtual image stack open):
* Select Plugins->Cluster->Batch->Trainable Segmentation 2D

 * Note: The image file(s) in question must exist within your cluster file root.

 * If the plugin cannot determine the file associated with your image, it will prompt you for a file cache directory.

* A dialog box will open, with the follow fields:
 * Classifier File:  A model file, as obtained by saving the classifier in Trainable Weka Segmentation
 * Block Size (px): The desired size of each image block, in pixels
 * Overlap (px): The number of pixels in the overlap between each block and its neighbors. This is done to prevent edge-effect problems for features generated from convolution-like operations.


* Click OK, then configure your cluster. Once the cluster has started, the image(s) will be blocked, segmented, then recombined.