As I have been working with Seg3D in electron microscopy extensively over the past month, I've come across several situations where new tools would be useful.

EM images, especially of neural tissue, are dense, with many objects packed closely together.  Usually, with thresholding and a connected components filter I can tease out large objects.  However, when it comes to following small processes, this approach breaks down quickly.  Additionally, the size of the processes and the inherent noise in the images makes eroding until the desired part of a mask is essentially disconnected impossible.  Usually, what I end up with is a good approximation of the process but still connected to adjacent processes.  It would be useful to have a tool where I could place two seed points, one in the part of the mask I want to keep and the other that should be disconnected.  The tool would then find the pixels closest to the two seed points that are also adjacent to one another, and then delete the pixel closest to the seed point of the part of the mask to be thrown away.  This process would iterate until the portion of the mask containing the unwanted seed point is disconnected, and then remove that portion of the mask.  I don't know if that makes any sense, or if it would be difficult to implement, but it would speed segmentation of these small processes by at least an order of magnitude.

Also, is there a way to invert the function of the polyline, such that it keeps everything inside it and throws everything else away?  That would help me to reduce the noise I'm dealing with in my masks.

Thanks!

Paul Holcomb
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