Supervised Nonparametric Image Parcellation

DOI: 10.1007/978-3-642-04271-3_130

ABSTRACT Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data
are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly
losing valuable information on inter-subject variability. This paper presents a novel framework for Supervised Nonparametric
Image Parcellation (SNIP). SNIP models the intensity and label images as samples of a joint distribution estimated from the
training data in a non-parametric fashion. By capitalizing on recently developed fast and robust pairwise image alignment
tools, SNIP employs the entire training data to segment a new image via Expectation Maximization. The use of multiple registrations increases robustness
to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with manual labels for the white
matter, cortex and subcortical structures. SNIP yields better segmentation than state-of-the-art algorithms in multiple regions
of interest.

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