Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%.  These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR

EyeArt + EyePACS: Automated Retinal Image Analysis For Diabetic Retinopathy Screening in a Telemedicine System

Telemedicine frameworks are key to screening the large, ever-growing diabetic population for preventable blindness due to diabetic retinopathy (DR). Integrating fully-automated screening systems in telemedicine frameworks will make DR screening more efficient, cost-effective, reproducible, and accessible. In this paper, we present the integration of EyeArt, an automated DR screening system, into EyePACS, a telemedicine system for DR screening used in diverse screening settings. EyeArt in- corporates novel image processing and analysis algorithms for assessing image gradability; enhancing images based on median filtering; detecting interest regions and localizing lesions based on multi-scale morphological analysis; and DR screening and thus achieves robustness to the large image variability seen in a telemedicine system such as EyePACS. EyeArt is implemented as a scalable, high-throughput cloud-based system to enable large-scale DR screening. We evaluate the safety and performance of EyeArt on a dataset with 434,023 images from 54,324 patient cases obtained from EyePACS. On this dataset, EyeArt’s screening sensitivity is 90% at specificity 60.8% and the area under the receiver operating characteristic curve (AUROC) is 0.883. In a setup where trained human graders review patient cases recommended for referral by EyeArt with low confidence, a workload reduction of 62% is possible. Therefore, EyeArt can be safely integrated into large real world telemedicine DR screening programs such as EyePACS helping reduce workload and increase efficiency and thus help in reducing vision loss due to DR through early detection and treatment

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis

Enhancing CNNs through the use of hand-crafted features in automated fundus image classification

Eye diseases such as diabetic retinopathy and diabetic macular edema pose a major threat in today’s world as they affect a significant portion of the global population. Therefore, it is of utmost importance to develop robust solutions that can accurately detect these diseases, especially in their early stages. However, current methods, based on hand-crafted features devised by experts, are not sufficiently accurate. Several solutions have been proposed that use deep learning techniques to improve the performance of such systems. However, they ignore the highly valuable hand-crafted features, that could contribute to more accurate prediction, which underlines the significance of our research. In this paper, we revisit the problem of combining these hand-crafted features with the features extracted by neural networks with the objective of delivering more accurate predictions. We systematically study several state-of-the-art neural networks and methods and propose a number of ways to integrate them into our framework. We show that we arrived at the conclusion that it is possible to achieve significantly better results and outperform networks that do not consider hand-crafted features using the proposed methods