New algorithms to characterize and classify ophthalmic images

Unprecedented advances in machine learning have led to a variety of algorithms for the remote evaluation of biomedical images, allowing for cost-effective early detection of diseases. In particular, a lot of efforts are focused on the development of reliable image analysis tools for the early diagnosis of eye diseases. Here we present several new methods for ophthalmic image analysis. We propose a machine learning algorithm for ordering images of the anterior chamber (optical coherence tomography, OCT), which extracts features that discriminate between healthy subjects and patients with angle-closure. We also present an algorithm to detect the OCT images that contain artifacts, and we show that removing these images from the data base improves the performance of the ordering algorithm. Finally, we present algorithms for the analysis of retina fundus images, which are able to segment the vessel network in the retina and extract features from the topological tree-like network structure. We show that these features discriminate between healthy subjects and those with glaucoma or diabetic retinopathy.