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

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 meas...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2019-02, Vol.9 (1), p.1157-1157, Article 1157
Main Authors: Amil, Pablo, González, Laura, Arrondo, Elena, Salinas, Cecilia, Guell, J. L., Masoller, Cristina, Parlitz, Ulrich
Format: Article
Language:English
Subjects:
59
631/114/1564
631/1647/245/2226
639/705/794
639/766/530
Algorithms
Annotations
Anterior chamber
Classification
Física
Glaucoma
Humanities and Social Sciences
Image processing
Imatges
Learning algorithms
Machine learning
Medical personnel
Methods
multidisciplinary
Noise
Processament
Science
Science (multidisciplinary)
Tomografia
Tomography
Àrees temàtiques de la UPC
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 applied to a large (~1000) database of anterior chamber OCT images of healthy subjects and patients with angle-closure and the resulting unsupervised ordering and classification is validated by two ophthalmologists.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-38136-8