Automated Identification of Diabetic Retinopathy: A Survey

Diabetes strikes when the pancreas stops to produce sufficient insulin, gradually disturbing the retina of the human eye, leading to diabetic retinopathy. The blood vessels in the retina become changed and have abnormality. Exudates are concealed, micro-aneurysms and haemorrhages occur in the retina of eye, which intern leads to blindness. The presence of these structures signifies the harshness of the disease. A systematized Diabetic Retinopathy screening system will enable the detection of lesions accurately, consequently facilitating the ophthalmologists. Micro-aneurysms are the initial clinical signs of diabetic retinopathy. Timely identification of diabetic retinopathy plays a major role in the success of managing the disease. The main task is to extract exudates, which are similar in color property and size of the optic disk; afterwards micro-aneurysms are alike in color and closeness with blood vessels. The primary objective of this review is to survey the methods, techniques potential benefits and limitations of automated detection of micro-aneurysm in order to better manage translation into clinical practice, based on extensive experience with systems used by opthalmologists treating diabetic retinopathy

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

Detection of microaneurysms in retinal images using an ensemble classifier

This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA) detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR) which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature). A new set of 32 MA groundtruth images (with a total of 256 labelled MAs) based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1) and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur

Incorporating spatial and temporal information for microaneurysm detection in retinal images

The retina of the human eye has the potential to reveal crucial information about several diseases such as diabetes. Several signs such as microaneurysms (MA) manifest themselves as early indicators of Diabetic Retinopathy (DR). Detection of these early signs is important from a clinical perspective in order to suggest appropriate treatment for DR patients. This work aims to improve the detection accuracy of MAs in colour fundus images. While it is expected that multiple images per eye are available in a clinical setup, proposed segmentation algorithms in the literature do not make use of these multiple images. This work introduces a novel MA detection algorithm and a framework for combining spatial and temporal images. A new MA detection method has been proposed which uses a Gaussian matched filter and an ensemble classifier with 70 features for the detection of candidates. The proposed method was evaluated on three public datasets (171 images in total) and has shown improvement in performance for two of the sets when compared to a state-of-the-art method. For lesion-based performance, the proposed method has achieved Retinopathy Online Challenge (ROC) scores of 0.3923, 2109 and 0.1523 in the MESSIDOR, DIARETDB1 and ROC datasets respectively. Based on the ensemble algorithm, a framework for the information combination is developed and consists of image alignment, detecting candidates with likelihood scores, matching candidates from aligned images, and finally fusing the scores from the aligned image pairs. This framework is used to combine information both spatially and temporally. A dataset of 320 images that consists of both spatial and temporal pairs was used for the evaluation. An improvement of performance by 2% is shown after combining spatial information. The framework is applied to temporal image pairs and the results of combining temporal information are analyzed and discussed