I am working in image segmentation with super pixels. My data is a large matrix describing various attributes of each stick of pixels (such as height, width and disparity). The data comes from an image taken with a stereo camera so for each stixel I can get a respective point in 3D. From these points I have built a graph where each vertex is the stixel ID, the edges represent proximity (if a stixel is a neighbour of another) and the weights are the euclidean distance. Since I must cluster by orientation planes I thought that the branches of an MST would give me an initial set of orientation planes and starting clusters. However, I don't know how to cluster the MST's branches using the weights. I also tried graph clustering with MCL in the full graph but the results where not very good. Is there any algorithm that could cluster a tree's branches? 
Branch clustering for an MST
algorithms;graph theory;graphs;image processing;weighted graphs