k-Nearest Neighbor Curves in Imaging Data Classification

k-Nearest Neighbor Curves in Imaging Data Classification

Background: Lung disease quantification via medical image analysis is classically difficult. We propose a method based on normalized nearest neighborhood distance classifications for comparing individual CT scan air-trapping distributions (representing 3D segmented parenchyma). Previously, between-i...

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Bibliographic Details
Main Authors: Yann Cabon, Carey Suehs, Sébastien Bommart, Isabelle Vachier, Gregory Marin, Arnaud Bourdin, Nicolas Molinari
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-05-01
Series:Frontiers in Applied Mathematics and Statistics
Subjects:
point pattern comparisons
imaging data
k-nearest neighbor curve
B-spline classifiers
CT scan
Online Access:https://www.frontiersin.org/article/10.3389/fams.2019.00022/full

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https://www.frontiersin.org/article/10.3389/fams.2019.00022/full

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