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Journal Article

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

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Parlitz,  Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

Amil, P., Gonzalez, L., Arrondo, E., Salinas, C., Guell, J. L., Masoller, C., et al. (2019). Unsupervised feature extraction of anterior chamber OCT images for ordering and classification. Scientific Reports, 9: 1157. doi:10.1038/s41598-018-38136-8.


Cite as: http://hdl.handle.net/21.11116/0000-0003-0305-4
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 applied to a large (similar to 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.