Exudate detection in color retinal images for mass screening of diabetic retinopathy

International audienceThe automatic detection of exudates in colour eye fundus images is an important task in applications such as diabetic retinopathy screening. The presented work has been undertaken in the framework of the TeleOphta project, whose main objective is to auto-matically detect normal exams in a tele-ophthalmology network, thus reducing the burden on the readers. A new clinical database, e-ophtha EX, containing precisely manually contoured exudates, is introduced. As opposed to previously available databases, e-ophtha EX is very heterogeneous. It contains images gathered within the OPHDIAT telemedicine network for diabetic retinopathy screening. Image definition, quality, as well as patients condition or the retinograph used for the acquisition, for example, are subject to important changes between different examinations. The proposed exudate detection method has been designed for this complex situation. We propose new preprocessing methods, which perform not only normalization and denoising tasks, but also de-tect reflections and artifacts in the image. A new candidates segmentation method, based on mathematical morphology, is proposed. These candidates are characterized using classical features, but also novel contextual features. Finally, a random forest algorithm is used to detect the exudates among the candidates. The method has been validated on the e-ophtha EX database, obtaining an AUC of 0.95. It has been also validated on other databases, obtaining an AUC between 0.93 and 0.95, outperforming state-of-the-art methods

Automatic Estimation of Tumor Vascularization Using Virtual Slides.

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A General Framework For Detecting Diabetic Retinopathy Lesions In Eye Fundus Images

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RetinOpTIC-Automatic Evaluation of Diabetic Retinopathy

International audiencePurpose: The RetinOpTIC project performs mass screening of color fundus images and assesses image quality and Diabetic Retinopathy (DR) grade. Algorithm performance is evaluated on the Messidor-2 image database.Methods: Based on artificial intelligence (AI) solutions, referable DR is detected using convolutional neural networks (CNNs). The solution includes first the automatic assessment of the quality of the photography, and then the DR grad

Spatial normalization of eye fundus images

International audienceThe development of digital retinal color imaging causes a substantial increase in the number and the size of retinal image databases. Image processing methods have been developed to help the specialists analyze these images. However, the heterogeneity of the databases, regarding image scale, contrast or quality, makes the design of generic image processing algorithms difficult. The presented work focuses on the spatial normalization of these images. The method is based on the definition of a size invariant in images. Unfortunately, the size of anatomical structures is either difficult to measure (e.g. the distance between optic disk and fovea requires a tricky segmentation of the fovea) or can change from one person to another (e.g. the optic disk size ranges from 1 to 2 mm). Neither does the resolution of images give a satisfactory solution, even if it is the approach used in the literature. We propose to use the diameter of the field of view in images as a size invariant. It is shown to give good results, provided that all the images have been acquired with the same aperture angle. OPHDIAT is a telemedicine network for diabetic retinopathy screening. Thousands of color eye fundus images have been collected, 70% of which have been classified as healthy by ophthalmologists. The TeleOphta project aims at performing a preliminary analysis of the images, in order to automatically filter out healthy images, and thus reduce the burden on specialists. The proposed method has been validated using images from OPHDIAT. Its results have been compared with those of the spatial normalization based on the manual measurement of the distance between the center of the optic disk and the center of the fovea. Results show a nearly perfect agreement between them

Spatial normalization of eye fundus images

International audienceThe development of digital retinal color imaging causes a substantial increase in the number and the size of retinal image databases. Image processing methods have been developed to help the specialists analyze these images. However, the heterogeneity of the databases, regarding image scale, contrast or quality, makes the design of generic image processing algorithms difficult. The presented work focuses on the spatial normalization of these images. The method is based on the definition of a size invariant in images. Unfortunately, the size of anatomical structures is either difficult to measure (e.g. the distance between optic disk and fovea requires a tricky segmentation of the fovea) or can change from one person to another (e.g. the optic disk size ranges from 1 to 2 mm). Neither does the resolution of images give a satisfactory solution, even if it is the approach used in the literature. We propose to use the diameter of the field of view in images as a size invariant. It is shown to give good results, provided that all the images have been acquired with the same aperture angle. OPHDIAT is a telemedicine network for diabetic retinopathy screening. Thousands of color eye fundus images have been collected, 70% of which have been classified as healthy by ophthalmologists. The TeleOphta project aims at performing a preliminary analysis of the images, in order to automatically filter out healthy images, and thus reduce the burden on specialists. The proposed method has been validated using images from OPHDIAT. Its results have been compared with those of the spatial normalization based on the manual measurement of the distance between the center of the optic disk and the center of the fovea. Results show a nearly perfect agreement between them

FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

International audienceThe Messidor database, which contains hundreds of eye fundus images, has been publicly distributed since 2008. It was created by the Messidor project in order to evaluate automatic lesion segmentation and diabetic retinopathy grading methods. Designing, producing and maintaining such a database entails significant costs. By publicly sharing it, one hopes to bring a valuable resource to the public research community. However, the real interest and benefit of the research community is not easy to quantify. We analyse here the feedback on the Messidor database, after more than 6 years of diffusion. This analysis should apply to other similar research databases

TeleOphta: Machine learning and image processing methods for teleophthalmology

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