Francisco Fumero

Universidad de La Laguna | ULL · Department of Systems Engineering, Automatic Control, Architecture and Computer Technology

Glaucoma, a leading cause of blindness, damages the optic nerve, making early diagnosis challenging due to no initial symptoms. Fundus eye images taken with a non-mydriatic retinograph help diagnose glaucoma by revealing structural changes, including the optic disc and cup. This research aims to thoroughly analyze saliency maps in interpreting conv...

Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional N...

Deep learning systems, especially in critical fields like medicine, suffer from a significant drawback, their black box nature, which lacks mechanisms for explaining or interpreting their decisions. In this regard, our research aims to evaluate the use of surrogate models for interpreting convolutional neural network (CNN) decisions in glaucoma dia...

The automatic location of the fovea is very useful for diagnosing retinal diseases. It is a complex problem for which different solutions have been proposed based on classical image processing and Deep Learning techniques. The method presented in this paper is based on histograms that combine spatial and color information in such a way that the spa...

Glaucoma is one of the world leading causes of irreversible blindness. Early detection is essential to delay its progression and prevent vision loss. An accurate segmentation of the cup region in retinal fundus images is necessary to obtain relevant measurements for the detection of glaucoma. In recent years, multiple methods have been developed to...

Purpose: The main objective of this study is to characterize the activation regions of three Deep Learning models using infrared images of the optic nerve of glaucoma patients. Methods: We have retrospectively collected a sample of patients with primary and secondary open-angle glaucoma and normal patients. The images in infrared were recorded with...

Glaucoma is the second leading cause of blindness and is the leading cause of irreversible blindness disease in the world. Early screening for glaucoma in the population is significant. Color fundus photography is the most cost effective imaging modality to screen for ocular diseases. Deep learning network is often used in color fundus image analys...

Purpose To determine the diagnostic generalizability of two deep learning models when trained only with images of the ganglion cell layer (GCL) of mild glaucoma. Methods We have collected a sample from patients with primary and secondary open-angle glaucoma and normal patients. The sample was divided into mild glaucoma (MD≤6 dB), and moderate-adva...

Objective To determine and compare the diagnostic precision in glaucoma of two deep learning models using infrared images of the optic nerve, eye fundus, and the ganglion cell layer (GCL). Methods We have selected a sample of normal and glaucoma patients. Three infrared images were registered with a spectral-domain optical coherence tomography (SD...

Resumen Objetivo Determinar y comparar la precisión diagnóstica en glaucoma de dos modelos de aprendizaje profundo, usando imágenes en infrarrojo del nervio óptico, del fondo de ojo y de la capa de células ganglionares (CCG). Métodos Hemos seleccionado una muestra de pacientes normales y con glaucoma. Se recogieron tres imágenes en infrarrojo con...

The first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference images for segmenting the optic disc, in recent yea...

A new method for automatic optic disc localization and segmentation is presented. The localization procedure combines vascular and brightness information to provide the best estimate of the optic disc center which is the starting point for the segmentation algorithm. A detection rate of 99.58% and 100% was achieved for the Messidor and ONHSD databa...

Code for automatic optic disc location and segmentation

An accurate detection of the cup region in retinal images is necessary to obtain relevant measurements for glaucoma detection. In this work, we present an Ant Colony Optimization-based method for optic cup segmentation in retinal fundus images. The artificial agents will construct their solutions influenced by a heuristic that combines the intensit...

Description of the method Laguna ONhE that measures the amount of hemoglobin at the optic nerve head from color fundus pictures. It shows the basis of the method and its application to glaucoma and other pathologies that affect the optic nerve. The amount of hemoglobin is one of the most important indexes in optic nerve perfusion. This chapter give...

Purpose: To calculate the amount of hemoglobin (Hb) in the optic nerve head (ONH), using superimposed color fundus images with disc, rim and cup boundaries obtained by OCT-Cirrus. Material and methods: We examined 100 healthy and 121 glaucomatous eyes using Oculus-Spark perimetry, Cirrus-OCT and Visucam (Zeiss) ONH color images. The Laguna ONhE...

Purpose:To calculate the amount of hemoglobin (Hb) in sectors of the optic nerve head (ONH) from stereoscopic color fundus images using the Laguna ONhE method and compare the results to the visual field evaluation and optical coherence tomography (OCT). Methods:Healthy eyes (n=87) and glaucoma eyes (n=71) underwent reliable Oculus Spark perimetry a...

Automatic marker selection for watershed segmentation is a difficult problem. Most of the existing procedures are intended for specific application fields and usually require some prior knowledge about the problem at hand. A simple and general method for generating markers is proposed. The markers are obtained from the splitting of the three-dimens...

To evaluate a new method for measuring haemoglobin (Hb) levels and quantifying the colour changes in the optic nerve head of multiple sclerosis (MS) patients to detect axonal loss and consequently optic disc atrophy. 40 MS patients and 40 age and sex-matched healthy subjects were included in this prospective cross-sectional study and underwent a fu...

Objective: Estimation of the error rate in the subjective determination of the optic nerve head edge and area. Method: 1) 169 images of optic nerve disc were evaluated by five experts for the defining of the edges in 8 positions (every 45°). 2) The estimated areas of 26 cases were compared with the measurements of the Cirrus Optical Coherence To...

Objective Estimation of the error rate in the subjective determination of the optic nerve head edge and area.Method1) 169 images of optic nerve disc were evaluated by five experts for the defining of the edges in 8 positions (every 45°). 2) The estimated areas of 26 cases were compared with the measurements of the Cirrus Optical Coherence Tomograph...

Automated diagnosis of glaucoma disease has been studied for years. A great amount of research work in this field has been focused on the analysis of retinal fundus images to localize, detect and evaluate the optic disc. An open fundus image database with accurate gold standards of the optic nerve head has been implemented. A variability measuremen...