Automated Walks using Machine Learning for Segmentation

Abstract

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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