Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty driven probabilistic voxel selection for image registration   This paper presents a novel probabilistic voxel selection strategy for
medical image registration in time-sensitive contexts, where the goal is
aggressive voxel sampling (e.g. using less than 1% of the total number) while
maintaining registration accuracy and low failure rate. We develop a Bayesian
framework whereby, first, a voxel sampling probability field (VSPF) is built
based on the uncertainty on the transformation parameters. We then describe a
practical, multi-scale registration algorithm, where, at each optimization
iteration, different voxel subsets are sampled based on the VSPF. The approach
maximizes accuracy without committing to a particular fixed subset of voxels.
The probabilistic sampling scheme developed is shown to manage the tradeoff
between the robustness of traditional random voxel selection (by permitting
more exploration) and the accuracy of fixed voxel selection (by permitting a
greater proportion of informative voxels).

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci