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.