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Unsupervised feature extraction of anterior chamber OCT images for ordering and classification

Author
Amil, P.; González, L.; Arrondo, E.; Salinas , C.; Güell, J.; Masoller, C.; Parlitz, U.
Type of activity
Journal article
Journal
Scientific reports
Date of publication
2019-02-04
Volume
9
First page
1
Last page
9
DOI
https://doi.org/10.1038/s41598-018-38136-8 Open in new window
Project funding
Advanced biomedical optical imaging and data analysis
Complex physical and biophysical systems: towards a comprehensive view of their dynamics and fluctuations
Repository
http://hdl.handle.net/2117/129421 Open in new window
URL
https://www.nature.com/articles/s41598-018-38136-8 Open in new window
Abstract
We propose an image processing method for ordering anterior chamber optical coherence tomography (OCT) images in a fully unsupervised manner. The method consists of three steps: Firstly we preprocess the images (filtering the noise, aligning and normalizing the resolution); secondly, a distance measure between images is computed for every pair of images; thirdly we apply a machine learning algorithm that exploits the distance measure to order the images in a two-dimensional plane. The method is ...
Citation
Amil, P. [et al.]. Unsupervised feature extraction of anterior chamber OCT images for ordering and classification. "Scientific reports", 4 Febrer 2019, vol. 9, p. 1-9.
Group of research
DONLL - Nonlinear dynamics, nonlinear optics and lasers

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