Agnès Chabouis

TL;DR: In this paper, a complete prototype for the automatic detection of normal examinations on a teleophthalmology network for diabetic retinopathy screening is presented, which combines pathological pattern mining methods, with specific lesion detection methods, to extract information from the images.

TL;DR: A complete prototype for the automatic detection of normal examinations on a teleophthalmology network for diabetic retinopathy screening is presented, which combines pathological pattern mining methods with specific lesion detection methods to extract information from the images.

TL;DR: New preprocessing methods are proposed, which perform not only normalization and denoising tasks, but also detect reflections and artifacts in the image, and a random forest algorithm is used to detect the exudates among the candidates.

TL;DR: A variation on usual data mining algorithms, originally designed to mine static data, is proposed so that contextual and visual data at adaptive granularity levels can be mined.

TL;DR: The presented work focuses on the spatial normalization of color eye fundus images collected by the TeleOphta project, and proposes to use the diameter of the field of view in images as a size invariant.