Automatic Cephalometric X-Ray Landmark Detection Challenge 2014: A tree-based algorithm
نویسندگان
چکیده
In this paper, we propose a machine learning based algorithm for the Automatic Cephalometric X-Ray Landmark Detection Challenge. We use Extremely Randomized Forests combined with simple pixel-based multiresolution features. Using 10-fold cross validation, detection rate for some landmarks is reaching 96% under 2.5mm. Results show high variability between the different landmarks: some landmarks are detected with high accuracy while some others appears to be more difficult to detect, probably due to the high variability of their appearance in the dataset.
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