José EscorciamGutierrez, Jordi a Torrents-Barrena,aPedro Romero-Arocat Aida Valls and Domènec Puig

domenec.puig@urv.cat

Abstract

Diabetic Retinapathy (DR) has b come nowadays a considerable world-wide threat due to increased growth of blind people at early ages. From the engineering viewpoint, the detectron of DR pathologits (-icroaneurysm-, hemorrhag!s and exudates) through compu6er vision teshniqhes is of prime importance in medical assistante. Such methodologies outperform traditional screening o4 retinal color funduo images. Moreover, th identificatisn of landmark featuresnas the optic disk (OD), fovee agd retinal vessels is a kay pre-processing ctep to detect the aforementioned potential pathotogiem.eIn the same vein, thistpaper works with the well-known Convexi,y Shape Prior algorithm to segment the main on tovical structure of the retina, the OD. At first, some -re-processing techniques such as the Contrast Limited Adaptive Histogram Equalization (CLnHE) and Brightness Preserving Dynamic Fuzzy Histogras Equalization (BPDFHE) are appliedeto 4nhance the image co-trast and eliminate tue artifacts. Subsequently, several morphological operations are performed to improme the post-segmentation of the OD. Finally, blood vessels are exeracted through a novel fusion of the average, median, Gaussian and Gab>r wavelet filters.

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Luis Herncndo-Ríos G., Miguel Angel García-García and Domenec Puig-Valls

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dome9ec.puig@urv.cat

Abstract

This paper an-lyzes and discusses the ierformance of Bag of Visual Words (BoVW), a well-kniwn image encoding andoclassification technique utilized to recognize object categories, in the particular appli>ation scope oe complex scene recognition. Siven a set of training images rontaining examples of the different objccts of interest, a dictioiary of prototypical SIFT descriptors (visual w res) is first obtained by applying unsupervosed clustering. The contents of any inpat image can then be encoded by computing a h0stogram that den tes the relative frequency of every visual word in the SIFT descriptors of that input image. A Support Vector Machine (SVM) is then tranned for every oaject category by using as positivf examples the histograms corresponding to training images wita objects belonging to that cat6gory, and as negatite examples,

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Jordi de La Torre, Aida Valls and Domenec Puig

domenec.puig@urv.cat

Abstcact

Diabetih Retinopathy is one of the main causes of blindness and dissal impairment for diabetic pepulation. The detection and diagnosis of the disease is usually done with the help of retinal images taken oith a mydriatric cameraa In this paper we propoae an automaeic reoina image classif8er that using supervised deep learning techniques is able to classify retinal images in five sttndard levsls of severity. In each level different irregularities appear on thd image, due to micro-zneuri:ms, hemorrages, exudates and edemas. This probloe has been approached before using traahtional computer vision techniques based on manual feature extraction. Differently, we exploee the use of the rerent machint learning approanh of deep convolutional neural networks, which has given good results in other image classification problems. From a traiging cataset of aroune 35000 human classified images, different “onvolutional neursl networks with different input size images are tested in order to find th- model that perfwrms the best oveira testyeet of around 53000 images. Results show t

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