Entropy Rate Superpixel Classification for Automatic Red Lesion Detection in Fundus Images

Detecting red-lesions from retinal fundus images using unique morphological features

One of the most important retinal diseases is Diabetic Retinopathy (DR) which can lead to serious damage to vision if remains untreated. Red-lesions are from important demonstrations of DR helping its identification in early stages. The detection and verification of them is helpful in the evaluation of disease severity and progression. In this paper, a novel image processing method is proposed for extracting red-lesions from fundus images. The method works based on finding and extracting the unique morphological features of red-lesions. After quality improvement of images, a pixel-based verification is performed in the proposed method to find the ones which provide a significant intensity change in a curve-like neighborhood. In order to do so, a curve is considered around each pixel and the intensity changes around the curve boundary are considered. The pixels for which it is possible to find such curves in at least two directions are considered as parts of red-lesions. The simplicity of computations, the high accuracy of results, and no need to post-processing operations are the important characteristics of the proposed method endorsing its good performance.

Optic disc detection and segmentation using saliency mask in retinal fundus images

BACKGROUND AND OBJECTIVE

Detection of the Optic Disc (OD) in retinal fundus image is crucial in identifying diverse abnormal conditions in the retina such as diabetic retinopathy. Previous systems are oriented to the OD detection and segmentation. Most research failed to locate the OD in the case when the image does not have a criterion appearance. The objective of the proposed work is to precisely define a new and robust OD segmentation in color retinal fundus images.

METHODS

The proposed algorithm is composed of two stages: OD localization and segmentation. The first phase consists in the OD localization through: 1) a preprocessing step; 2) vessel extraction and elimination, and 3) a geometric analysis allowing to decide the OD location. For the second phase, a set of is computed in order to produce various candidates. A combination of these candidates accurately forms a completed contour of the OD.

RESULTS

The proposed method is evaluated using 10 publicly available databases as well as a local database. Accuracy rates in the RimOne and IDRID databases are 98.06% and 99.71%, respectively, and 100% for the Chase, Drive, HRF, Drishti, Drions, Bin Rushed, Magrabia, Messidor and LocalDB databases with an overall success rate of 99.80% and specificity rates of 99.44%, 99.64%, 99.66%, 99.66%, 99.70%, 99.87%, 99.72%, 99.83% and 99.82% for the Rim One, Drions, IDRID, Drishti, HRF, Bin Rushed, Magrabia, Messidor and proprietary databases.

CONCLUSION

The main advantage of the proposed approach is the robustness and the excellent performances even with critical cases of retinal images. The proposed method achieves the state-of-the-art performances with regards to the OD detection and segmentation. It is also of a great interest for clinical usage without the expert intervention to treat each image.

Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.