  Matching cells over time has long been the most difficult step in cell
tracking. In this paper, we approach this problem by recasting it as a
classification problem. We construct a feature set for each cell, and compute a
feature difference vector between a cell in the current frame and a cell in a
previous frame. Then we determine whether the two cells represent the same cell
over time by training decision trees as our binary classifiers. With the output
of decision trees, we are able to formulate an assignment problem for our cell
association task and solve it using a modified version of the Hungarian
algorithm.
