Tracking Tetrahymena pyriformis cells using decision trees
Pattern Recognition(2012)
摘要
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.
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关键词
cell matching,cellular biophysics,feature difference vector,image matching,cell association task,biology computing,feature extraction,tetrahymena pyriformis cell tracking,image classification,feature set,hungarian algorithm,data mining,binary classifier,decision tree,decision trees,assignment problem
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