Detection Of Retinal Hemorrhages Research Articles

Beyond the AJR: Deep Learning Shows Promise in the Detection of Retinal Hemorrhage on Pediatric Head CT.

Beyond the AJR: Deep Learning Shows Promise in the Detection of Retinal Hemorrhage on Pediatric Head CT.

A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening.

To explore the performance in diabetic retinopathy (DR) screening of artificial intelligence (AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. Overall, 630 eyes were included from three centers and screened by a handheld camera (Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR. Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range (1.47, 1.48, and 1.40) than conventional tabletop cameras (1.30, 1.28, and 1.18; P<0.001). Detection of retinal hemorrhage, hard exudation, and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1% (95%CI: 72.1%-92.2%) and 97.4% (95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory; however, Phoebus showed a high sensitivity (88.2%, 95%CI: 79.4%-97.1%) and low specificity (40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR. The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.

Neuroimaging of retinal hemorrhage utilizing adjunct orbital susceptibility-weighted imaging

Retinal hemorrhages are an integral part of the evaluation of abusive head trauma (AHT). Timely detection of retinal hemorrhage not only facilitates the diagnosis of AHT, but has the potential to prevent further abuse to the child and the siblings and to identify the abuser. The gold standard for diagnosing retinal hemorrhage is a dilated fundoscopy exam, which requires pharmacological dilation. As such, there is a small percentage of patients for whom the dilated fundoscopy exam might be delayed. Evolving literature suggests that MRI, specifically susceptibility-weighted imaging (SWI), of the orbits might provide an alternative diagnostic tool for noninvasively detecting retinal hemorrhages, particularly when there is a delay in administering the dilated fundoscopy exam. In this paper we review the utility of SWI for detecting retinal hemorrhages in abusive head trauma, including discussion of diagnostic limitations and future research.

Automated Detection of Retinal Hemorrhage based on Supervised Classifiers

Supervised machine learning algorithm based retinal hemorrhage detection and classification is presented. For developing an automated diabetic retinopathy screening system, efficient detection of retinal hemorrhage is important. Splat, which is a high level entity in image segmentation is used to mark out hemorrhage in the pre-processed fundus image. Here, color images of retina are portioned into different segments (splats) covereing the whole image. With the help of splat level and GLCM features extracted from the splats, three classifiers are trained and tested using the relevant features. The ground-truth is established with the help of a retinal expert and using dataset and clinical images the validation was done. The output obtained using the three classifiers had more than 96 % sensitivity and accuracy.

Automated Detection of Retinal Hemorrhage based on Supervised Classifiers

Supervised machine learning algorithm based retinal hemorrhage detection and classification is presented. For developing an automated diabetic retinopathy screening system, efficient detection of retinal hemorrhage is important. Splat, which is a high level entity in image segmentation is used to mark out hemorrhage in the pre-processed fundus image. Here, color images of retina are portioned into different segments (splats) covereing the whole image. With the help of splat level and GLCM features extracted from the splats, three classifiers are trained and tested using the relevant features. The ground-truth is established with the help of a retinal expert and using dataset and clinical images the validation was done. The output obtained using the three classifiers had more than 96 % sensitivity and accuracy.

Detection of retinal hemorrhage from fundus images using ANFIS classifier and MRG segmentation

Diabetic Retinopathy is a medicinal condition in which the retina is impaired based on fluid breaks from veins into the retina. The occurrence of hemorrhages in the retina is the most prompt effect of diabetic retinopathy. The number and state of hemorrhages are used to display the significance of the disorder. This research paper analyzed hemorrhage detection in retinal fundus images using classifier and segmentation methods. All the database images into the pre-processing steps and some meaning full features are extracted from the images. Then ANFIS classifier utilized to normaland abnormal images, this abnormal category into the hemorrhage detection process with help of segmentation technique. Here Region growing (RG) with threshold optimization techniques are considered its known as Modified RG (MRG) to get the maximum accuracy in the hemorrhage segmenting process. As regards the threshold optimization, Grey Wolf Optimization (GWO) technique used, this proposed work compared to our existing work getting maximum accuracy, sensitivity and specificity performance metrics.

Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients

Diabetic retinopathy (DR) is one of the main retinal abnormalities which is asymptomatic and is the main cause of vision loss in diabetic patients. The computer-aided diagnosis systems based on image processing not only facilitate the doctor but also decrease the diagnosis time. This work represents the automated detection of one of the red lesion, i.e., hemorrhages, which are one of the most distinctive signs of retinal diseases in diabetic patients. In the proposed method, the foremost step is to enhance the image quality by eliminating the background noise and nonuniform illumination. This is achieved by applying the methods such as image contrast enhancement and normalization. The subsequent step is to segment the blood vessels from hemorrhages (using scale-based method) as both of them have the same color. The last step is to delineate the hemorrhages by exploiting the gamma correction and global thresholding techniques. The proposed method has achieved specificity (SP) of 84%, sensitivity (SN) of 87%, and an accuracy of 89 % on the DIARETDB1 database.

Detection of Retinal Hemorrhage in Color Fundus Image

Detection of Retinal Hemorrhage in Color Fundus Image

Detection of Diabetic Retinopathy using Splat Feature Classification in Fundus Image

Automated detection of retinal hemorrhages in fundus image is crucial step towards early detection or screening is difficult among large population. A novel splat feature classification method is introduced to detect retinal hemorrhages. Classification is been achieved through supervised learning approaches. The performance of sensitivity and specificity is been improved while processing with retinal hemorrhages than with lesions. An area under receiver operating characteristics curve (ROC) of 0.96 can be achieved at splat level and 0.87 at image level. General Terms : Supervised Classification

Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images

A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into nonoverlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A set of features is extracted from each splat to describe its characteristics relative to its surroundings, employing responses from a variety of filter bank, interactions with neighboring splats, and shape and texture information. An optimal subset of splat features is selected by a filter approach followed by a wrapper approach. A classifier is trained with splat-based expert annotations and evaluated on the publicly available Messidor dataset. An area under the receiver operating characteristic curve of 0.96 is achieved at the splat level and 0.87 at the image level. While we are focused on retinal hemorrhage detection, our approach has potential to be applied to other object detection tasks.

Automated Detection of Malarial Retinopathy-Associated Retinal Hemorrhages

To develop an automated method for the detection of retinal hemorrhages on color fundus images to characterize malarial retinopathy, which may help in the assessment of patients with cerebral malaria. A fundus image dataset from 14 patients (200 fundus images, with an average of 14 images per patient) previously diagnosed with malarial retinopathy was examined. We developed a pattern recognition-based algorithm, which extracted features from image watershed regions called splats (tobogganing). A reference standard was obtained by manual segmentation of hemorrhages, which assigned a label to each splat. The splat features with the associated splat label were used to train a linear k-nearest neighbor classifier that learnt the color properties of hemorrhages and identified the splats belonging to hemorrhages in a test dataset. In a crossover design experiment, data from 12 patients were used for training and data from two patients were used for testing, with 14 different permutations; and the derived sensitivity and specificity values were averaged. The experiment resulted in hemorrhage detection sensitivities in terms of splats as 80.83%, and in terms of lesions as 84.84%. The splat-based specificity was 96.67%, whereas for the lesion-based analysis, an average of three false positives was obtained per image. The area under the receiver operating characteristic curve was reported as 0.9148 for splat-based, and as 0.9030 for lesion-based analysis. The method provides an automated means of detecting retinal hemorrhages associated with malarial retinopathy. The results matched well with the reference standard. With further development, this technique may provide automated assistance for screening and quantification of malarial retinopathy.

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

Image processing of a fundus image is performed for the early detection of diabetic retinopathy. Recently, several studies have proposed that the use of a morphological filter may help extract hemorrhages from the fundus image; however, extraction of hemorrhages using template matching with templates of various shapes has not been reported. In our study, we applied hue saturation value brightness correction and contrast-limited adaptive histogram equalization to fundus images. Then, using template matching with normalized cross-correlation, the candidate hemorrhages were extracted. Region growing thereafter reconstructed the shape of the hemorrhages which enabled us to calculate the size of the hemorrhages. To reduce the number of false positives, compactness and the ratio of bounding boxes were used. We also used the 5 × 5 kernel value of the hemorrhage and a foveal filter as other methods of false positive reduction in our study. In addition, we analyzed the cause of false positive (FP) and false negative in the detection of retinal hemorrhage. Combining template matching in various ways, our program achieved a sensitivity of 85% at 4.0 FPs per image. The result of our research may help the clinician in the diagnosis of diabetic retinopathy and might be a useful tool for early detection of diabetic retinopathy progression especially in the telemedicine.