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[Abstract] Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario for the diagnosis of relevant eye diseases like the vitreomacular traction or the diabetic retinopathy. The identification of the epiretinal membrane (ERM) is a relevant issue as its presence constitutes a symptom of diseases like the macular edema, deteriorating the vision quality of the patients. This work presents an automatic methodology for the identification of the ERM presence in OCT scans. Initially, a complete and heterogeneous set of features was defined to capture the properties of the ERM in the OCT scans. Selected features went through a feature selection process to further improve the method efficiency. Additionally, representative classifiers were trained and tested to measure the suitability of the proposed approach. The method was tested with a dataset of 285 OCT scans labeled by a specialist. In particular, 3,600 samples were equally extracted from the dataset, representing zones with and without ERM presence. Different experiments were conducted to reach the most suitable approach. Finally, selected classifiers were trained and compared using different metrics, providing in the best configuration an accuracy of 89.35%. Ministerio de Economía, Industria y Competitividad, Gobierno de España (DPI2015-69948-R); Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia (ED431C 2016-047, ED431G/01); Instituto de Salud Carlos III (ISCIII) (DTS18/00136). Xunta de Galicia; ED431C 2016-047 Xunta de Galicia; ED431G/01

[Abstract] The analysis of remote sensing images represents a highly important issue to be performed in many relevant fields such as climate change studies or land cover mapping. Traditional proposals usually identify the land cover classes from general related groups such as different tree species or different crop varieties. Additionally, these proposals commonly use information from a precise time span or season, not accounting for the variability of the data over the entire year, specially in regions with several seasons. In this work, we propose a multi-temporal classification system to identify and represent diverse land cover classes over any period of the entire year by using Sentinel-2 satellite image data. To this end, 526 representative samples were labelled from 5 complex and variable different land cover types over the Special Area of Conservation (SAC) Betanzos-Mandeo in the northwest of the Iberian Peninsula. The method achieves a satisfactory mean accuracy value of 84.0% for the testing set using the best configuration with a radial Support Vector Machine classifier. This system will be used in the study of the population connectivity of two threatened herptiles, but it can be easily extended to other species of interest in the future. Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431C 2016-047

Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM&rsquo, s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM&rsquo, s presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM&rsquo, s presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91 . 9 % . Regarding the post-processing stage, mean specificity values of 91 . 9 % and 99 . 0 % were obtained from volumes with and without the ERM&rsquo, s presence, respectively.

This work presents an automatic method to characterize the presence or absence of the epiretinal membrane (ERM) in Optical Coherence Tomography (OCT) images. To this end, a predefined set of classifiers is used on multiple local-based feature vectors which represent the inner limiting membrane (ILM), the layer of the retina where the ERM can be present.

Optical Coherence Tomography (OCT) is a common imaging technique for the detection and analysis of optical diseases, since it is a non invasive method that generates in vivo a cross-sectional visualization of the retinal tissues. These characteristics contributed to the use of OCT imaging in the analysis of pathologies as, for instance, vitreomacular traction, age-related macular degeneration or hypertension. Among its applications, OCT imaging can be used in the detection of any present epiretinal membrane section in the retina, a critical issue to prevent further complications caused by this pathology.

This work presents a novel approach for automatic detection of the epiretinal membrane in Optical Coherence Tomography (OCT) images. A tool able to detect this pathology is very valued since it can prevent further ocular damage by doing an early detection. This approach is based in the location of the inner limiting membrane (ILM) layers of the retina. Then, the detected locations are classified using a local-feature based vector in order to determine presence of the membrane. Different tests are run and compared to establish the appropriateness of the approach as well as its practical validity.