Enhancing retinal image by the Contourlet transform

The evaluation of retinal images is widely used to help doctors diagnose many diseases, such as diabetes or hypertension. Due to the acquisition process, retinal images often have low grey level contrast and dynamic range. This problem may seriously affect the diagnostic procedure and its results. Here we present a new multi-scale method for retinal image contrast enhancement based on the Contourlet transform. The Contourlet transform has better performance in representing edges than wavelets for its anisotropy and directionality, and is therefore well-suited for multi-scale edge enhancement. We modify the Contourlet coefficients in corresponding subbands via a nonlinear function and take the noise into account for more precise reconstruction and better visualization. We compare this approach with enhancement based on the Wavelet transform, Histogram Equalization, Local Normalization and Linear Unsharp Masking. The application of this method on images from the DRIVE database showed that the proposed approach outperforms other enhancement methods on low contrast and dynamic range images, with an encouraging improvement, and might be helpful for vessel segmentation.