Detection of microaneurysms and hemorrhages based on improved Hessian matrix

Purpose Knowing the early lesion detection of fundus images is very important to prevent blindness, and accurate lesion segmentation can provide doctors with diagnostic evidence. This study proposes a method based on improved Hessian matrix eigenvalue analysis to detect microaneurysms and hemorrhages in the fundus images of diabetic patients. Methods A two-step method including identification of lesion candidate regions and classification of candidate regions is adopted. In the first step, the method of eigenvalue analysis based on the improved hessian matrix was applied to enhance the image preprocessed. A dual-threshold method was used for segmentation. Then, blood vessels were gradually removed to obtain the lesion candidate regions. In the second step, all candidates were classified into three categories: microaneurysms, hemorrhages and the others. Results The proposed method has achieved a better performance compared with the existing algorithms on accuracy rates. The classification accuracy rates of microaneurysms and hemorrhages obtained by using our method were 94.4% and 94.0%, respectively, while the classification accuracy rates obtained by using Frangi’s filter based on the Hessian matrix to enhance the image were 90.9% and 92.1%. Conclusion This study demonstrated a methodology for enhancing images by using eigenvalue analysis based on the improved Hessian matrix and segmentation by using double thresholds. The proposed method is beneficial to improve the detection accuracy of microaneurysms and hemorrhages in fundus images.