Learn More
The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology(More)
The algorithm proposed in this paper allows to segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method used for the extraction of the optic disc contour is based on a variant of the watershed transformation, the(More)
As Computer-Assisted Language Learning (CALL) has taken an important role in foreign language teaching and learning, not only is concrete data about the usefulness of technology-mediated environments for these purposes necessary, but also how the learning process is improved in such environments when learner training for CALL. The objective of this paper is(More)
This paper describes a new approach to determine vascular skeleton in retinal images. This approach is based on mathematical morphology along with curvature evaluation. In particular, a variant of the watershed transformation, the stochastic watershed, is applied to extract the vessel center-line. Its goal is to obtain directly the skeleton of the retinal(More)
This paper investigates discrimination capabilities in the texture of fundus images to differentiate between pathological and healthy images. For this purpose, the performance of local binary patterns (LBP) as a texture descriptor for retinal images has been explored and compared with other descriptors such as LBP filtering and local phase quantization. The(More)
Glaucoma is an asymptomatic eye disease and one of the major causes of irreversible blindness worldwide. For this reason, there have been significant advances in automatic screening tools for early detection. In this paper, an automatic glaucoma diagnosis algorithm based on retinal fundus image is presented. This algorithm uses anatomical characteristics(More)
This paper describes how to construct a probability map using sparse representation and dictionary learning to indicate the probability of each optic disk pixel of belonging to the optic cup. This probability map will be used in the future as input to a method for automatically detecting glaucoma from color fundus images. The probability map was obtained(More)
This work focuses on differentiating between pathological and healthy fundus images. The goal is to distinguish between diabetic retinopathy (DR), age-related macular degeneration (AMD) and normal images by analysing the texture of the retina background. Local Binary Patterns (LBP) are used as texture descriptors. The two class problems DR vs. normal and(More)