Accuracy For Hemorrhage Research Articles

COMPUTER HYBRID SYSTEM OF HEMORRHAGE (HES) DETECTION USED FOR AIDED DIAGNOSIS OF DIABETIC RETINOPATHY

Diabetes cause’s metabolic and physiological abnormalities in the retina and the changes suggest a role for inflammation in the development of diabetic retinopathy. Abnormal blood vessels can form in the back of the eye of a person with diabetes. These new blood vessels are weaker and prone to breaking and causing hemorrhage (HEs). Diabetic retinopathy (DR) accounts for 31.5–54% of all cases of vitreous hemorrhage in adults in the world. Therefore, detection of HEs is still a challenging factor task for computer-aided diagnostics of DR. Many researchers have developed advanced algorithms of hemorrhages detection using fundus images. In this paper, a robust and computationally efficient approach for HEs with different shape and size detection and classification is presented. First, brightness correction and contrast enhancement are applied to fundus images. Second, candidate hemorrhages are extracted by using an unsupervised classification algorithm. Third, an approach based on mathematical morphology is carried out for vascular network and macula segmentation. Finally, a total of 13 HEs features are considered in this study and selected for classification. The proposed method is evaluated on 419 fundus images of DIARETDB0, DIARETDB1 and MESSIDOR databases. Experimental results show that overall average sensitivity, specificity, predictive value and accuracy for hemorrhage in lesion level are 98.90%, 99.66%, 97.63% and 99.56%, respectively. The results show that the proposed method outperforms other state-of-the-art methods in detection of hemorrhages. These results indicate that this new method may improve the performance of diagnosis of DR system.

Hemorrhage detection in fundus image based on 2D Gaussian fitting and human visual characteristics

Hemorrhage is one of the early symptoms of diabetic retinopathy, so accurate detection of hemorrhage has important significance in building automatic screening system of diabetic retinopathy. In this paper, a method based on two dimensional (2D) Gaussian fitting and human visual characteristics is proposed. Firstly, brightness correction and contrast limited adaptive histogram equalization are used to preprocess original color fundus image. Secondly, candidate hemorrhages are extracted based on background estimation and watershed segmentation. Thirdly, 2D Gaussian fitting and human visual characteristics are used to extract visual features of candidate hemorrhages. Finally, hemorrhages are obtained from candidate hemorrhages based on visual features. The proposed method is evaluated on 219 fundus images of DIARETDB database. Experimental results show that overall average sensitivity, specificity and accuracy for hemorrhage in image level are 100%, 82% and 95.42% respectively, and overall average sensitivity and positive predictive value for hemorrhage in lesion level are 94.01% and 90.30% respectively. The results show that this method can realize automatic detection of hemorrhages in fundus image with high accuracy.