Retinal Image Graph-Cut Segmentation Algorithm Using Multiscale Hessian-Enhancement-Based Nonlocal Mean Filter

Abstract EN

We propose a new method to enhance and extract the retinal vessels.

First, we employ a multiscale Hessian-based filter to compute the maximum response of vessel likeness function for each pixel.

By this step, blood vessels of different widths are significantly enhanced.

Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time.

After that, a radial gradient symmetry transformation is adopted to suppress the nonvessel structures.

Finally, an accurate graph-cut segmentation step is performed using the result of previous symmetry transformation as an initial.

We test the proposed approach on the publicly available databases: DRIVE.

The experimental results show that our method is quite effective.