Automatic Segmentation of Exudates in Ocular Images using Ensembles of Aperture Filters and Logistic Regression

Hard and soft exudates are the main signs of diabetic macular edema (DME). The segmentation of both kinds of exudates generates valuable information not only for the diagnosis of DME, but also for treatment, which helps to avoid vision loss and blindness. In this paper, we propose a new algorithm for the automatic segmentation of exudates in ocular fundus images. The proposed algorithm is based on ensembles of aperture filters that detect exudate candidates and remove major blood vessels from the processed images. Then, logistic regression is used to classify each candidate as either exudate or non-exudate based on a vector of 31 features that characterize each potensial lesion. Finally, we tested the performance of the proposed algorithm using the images in the public HEI-MED database.Fil: Benalcazar Palacios, Marco Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación; EcuadorFil: Brun, Marcel. Universidad Nacional de Mar del Plata; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata; Argentin

Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.Comment: Preprint submitted to Journal of Medical Imaging | SPIE (Tue, Jul 28, 2017

Detection of Hard Exudates in Retinal Fundus Images using Deep Learning

Diabetic Retinopathy (DR) is a retinal disorder that affects the people having diabetes mellitus for a long time (20 years). DR is one of the main reasons for the preventable blindness all over the world. If not detected early the patient may progress to severe stages of irreversible blindness. Lack of Ophthalmologists poses a serious problem for the growing diabetes patients. It is advised to develop an automated DR screening system to assist the Ophthalmologist in decision making. Hard exudates develop when DR is present. It is important to detect hard exudates in order to detect DR in an early stage. Research has been done to detect hard exudates using regular image processing techniques and Machine Learning techniques. Here, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina.Comment: 5 Pages, 3 figures, 2 tables, International Conference on Systems, Computation, Automation and Networking http://icscan.in

Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks

We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm have the following advantages: (1) Our method can point out the location and type of lesions in the fundus images, as well as giving the severity grades of DR. Moreover, since retina lesions and DR severity appear with different scales in fundus images, the integration of both local and global networks learn more complete and specific features for DR analysis. (2) By introducing imbalanced weighting map, more attentions will be given to lesion patches for DR grading, which significantly improve the performance of the proposed algorithm. In this study, we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our local lesion detection net achieve comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly improve the capability of our DCNN-based DR grading algorithm

The evidence for automated grading in diabetic retinopathy screening

Peer reviewedPostprin

Lesion boundary segmentation using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth