Nonlinear retinal image enhancement for vessel detection - Wang, Xiaohong (2019)
Retinal vessel detection is an essential part of the computer-aided diagnosis of eye diseases. Due to non-perfect imaging environment, retinal images often appear with intensity variations and artificial noises. This work proposes a two-step nonlinear retinal image enhancement to compensate for those imperfections of retinal images. The first step reduces intensity fluctuations of the image and the second step attenuates impulsive noise while preserving retinal vessels. Classification on the feature vector extracted from the enhanced retinal images is performed by using a linear SVM classifier. Experimental results demonstrate that the proposed method of two-step nonlinear image enhancement visibly improves the vessel detection performance, achieving better accuracy than that without enhancement process on the both DRIVE and STARE databases.