Improvement of automated detection method of hemorrhages in fundus images.

This paper describes an improved method for detecting hemorrhages in fundus images. The detection of hemorrhages is one of the important factors in the early diagnosis of diabetic retinopathy. So, we had suggested several methods for detecting abnormalities in fundus images, but our methods had some problems. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected using density analysis. Finally, false positives were removed by using rule-based method and 3 Mahalanobis distance classifiers with a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases were 80% and 80%, respectively.