Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening

Abstract

The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

Automated retinal lesion detection via image saliency analysis

“…A large number of studies, that is, have performed lesion detection on prevalence of referable at the image level, but it is difficult to understand the criteria for selecting true positives and false negatives. In the study of MA detection, the sensitivity values against the average number of false positives per image (FPI) was used to measure performance .…”

“…Dai et al employed gradient vector analysis and a class‐imbalance classifier to determine MA candidates. Seoud et al generated a new set of shape features called dynamic shape features to detect dark lesions from retinal images. Dashtbozorg et al used a gradient weighting‐based iterative thresholding approach and a boosting classifier to locate MA.…”

“…The human retina is a window that allows clinicians to study retinal conditions such as diabetic retinopathy (DR) and malarial retinopathy (MR), as well as other systematic conditions such as cardiovascular diseases and stroke with noninvasive imaging techniques due to its transparency in nature. In particular, DR is a leading cause of vision impairment and loss in the working‐age population which affects nearly 500 million of people with diabetes worldwide. The severity of DR is usually determined by identifying specific features, such as exudates (EX), microaneurysms (MA), and hemorrhages (HE) in retinal color fundus (CF) images.…”

Red Lesion Detection Using Hough Transform and KNN Classifier for Diabetic Retinopathy Screening

“…In proposed system, more features are selected to discriminate between vessels and lesions (see Fig.10). Most of the lesions are not detected when using DSFs [3]. This problem is solved in the proposed system.…”

Detection of Microaneurysms and Hemorrhages in Fundus Image for Glaucoma Diagnosis

“…The fundus images with DR have a part of the eye tissues which would be damaged already is accurately said as red lesion. The retinal red lesions are the major signs of DR and it causes swelling in the retina blood vessels called as microaneurysms [5]. A mass cell accumulation occurs in retina and fluffy patches may also occur in case of bright lesions called hemorrhages.…”