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Unsupervised feature extraction of anterior chamber OCT images for ordering and classification
Pablo Amil1, Laura González2, Elena Arrondo2
1Universitat Politècnica de Catalunya, Rambla Sant Nebridi 22, 08222, Terrassa, Spain.
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 (~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.
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