GEOMETRICAL GRAPH MATCHING USING MONTE CARLO TREE SEARCH

Many medical images contain graph-like geometrical structures such as blood vessels and neuronal networks. We present an algorithm for matching geometrical graphs, in order to quickly and robustly align such images. We use a sampling-based curve descriptor to prune dissimilar edges. The matching is modeled as a single-player game, growing the matching from a random initial correspondence. The coarse global solution is found using a Monte Carlo Tree Search and then refined locally. We show experimentally that our approach finds the correct matching in all tested datasets and is the fastest of all global methods.