A new approach to two-dimensional filter for segmenting retinal vascular network from fundus images of premature born

There is a wide interest in the segmentation of retinal vessel structure from fundus images of preterm infants. This has lead to focus on the processing of dark or low resolution images acquired with portable medical cameras. The concept of signal matched filter (MF) has been used to recognize the retinal vascular structure and the matched filter and first-order derivative of Gaussian (MFFDOG) method was introduced to get a binarized vascular network. In this paper we present a new variation of MF for segmenting retinal vessels network. We apply the original MF to distinguish the vessels network in a gray scale image. Then we obtain the vascular network as a binary image using an adaptive thresholding based on our denominated hard kernel (MFHK). We have also generalized the application of matched filter with any filter function and any domain. We assessed both methods MFFDOG and MFHK in a set of fifty images of children with retinopathy of prematurity (ROP).