Automated Walks using Machine Learning for Segmentation
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3437
This paper describes an automated algorithm for segmentation
of brain structures (CSF, white matter, and gray matter) in MR
images. We employ machine learning, i.e. k-Nearest Neighbors, of features
derived from k-means, Canny edge detection, and Tourist Walks
to fully automate the seeding process of the Random Walker algorithm.
We test our methods on a dataset of 12 diabetes patients with atrophy
and varying degrees of white matter lesions provided by the MRBrainS13
Challenge, and find encouraging segmentation performance.

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Categories: Classification, Segmentation
Keywords: Random Walker, Tourist Walks, Machine Learning, k-NN, k-Means, Edge Detection
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