Back to Search
Start Over
k-Nearest Neighbor Curves in Imaging Data Classification
- Source :
- Frontiers in Applied Mathematics and Statistics, Frontiers in Applied Mathematics and Statistics, Frontiers Media S.A, 2019, 5, ⟨10.3389/fams.2019.00022⟩, Frontiers in Applied Mathematics and Statistics, 2019, 5, ⟨10.3389/fams.2019.00022⟩, Frontiers in Applied Mathematics and Statistics, Vol 5 (2019)
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- 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-image comparisons were precluded by the variation inherent to parenchyma segmentations, the dimensions of which are patient- and image-specific by nature.Method: Nearest neighbor distance estimations are normalized by a theoretical distance according to the uniform distribution of air trapping. This normalization renders images (of different sizes, shapes, and/or densities) comparable. The estimated distances for the k-nearest neighbor describe the proximity of point patterns over the image. Our approach assumes and requires a defined homogeneous space; therefore, a completion pretreatment is applied beforehand.Results: Model robustness is characterized via simulation in order to verify that the required initial transformations do not bias uniformly sampled results. Additional simulations were performed to assess the discriminant power of the method for different point pattern profiles. Simulation results demonstrate that the method robustly recognizes pattern dissimilarity. Finally, the model is applied on real data for illustrative purposes.Conclusion: We demonstrate that a parenchyma-cuboid completion method provides the means of characterizing air-trapping patterns in a chosen segmentation and, importantly, comparing such patterns between patients and images.
- Subjects :
- CT scan
Normalization (statistics)
Statistics and Probability
Computer science
[SDV]Life Sciences [q-bio]
Computed tomography
01 natural sciences
Imaging data
030218 nuclear medicine & medical imaging
k-nearest neighbors algorithm
B-spline classifiers
010104 statistics & probability
03 medical and health sciences
imaging data
0302 clinical medicine
Robustness (computer science)
point pattern comparisons
medicine
Segmentation
0101 mathematics
ComputingMilieux_MISCELLANEOUS
medicine.diagnostic_test
business.industry
lcsh:T57-57.97
Applied Mathematics
Pattern recognition
[SDV] Life Sciences [q-bio]
Discriminant
Lung disease
k-nearest neighbor curve
lcsh:Applied mathematics. Quantitative methods
Artificial intelligence
lcsh:Probabilities. Mathematical statistics
lcsh:QA273-280
business
Subjects
Details
- Language :
- English
- ISSN :
- 22974687
- Database :
- OpenAIRE
- Journal :
- Frontiers in Applied Mathematics and Statistics, Frontiers in Applied Mathematics and Statistics, Frontiers Media S.A, 2019, 5, ⟨10.3389/fams.2019.00022⟩, Frontiers in Applied Mathematics and Statistics, 2019, 5, ⟨10.3389/fams.2019.00022⟩, Frontiers in Applied Mathematics and Statistics, Vol 5 (2019)
- Accession number :
- edsair.doi.dedup.....a880887176903d0d9a2f64071e64956c