Exudates and Blood Vessel Segmentation in Eye Fundus Images using the Fourier and Cosine Discrete Transforms

Resumen

This paper presents a new method using discrete transforms to segment blood vessels and exudates in eye fundus color images. To obtain the desired segmentation, an illumination correction is previously done based on a homomorphic filter because of the uneven illuminance in the eye fundus image. To distinguish foreground objects from the background, we propose a super-Gaussian bandpass filter in the discrete cosine transform DCT domain. These filters are applied on the green channel that contains information to segment pathologies. To segment exudates in the filtered DCT image, a gamma correction is applied to enhance foreground objects; in the resulting image, the Otsu's global threshold method is applied, after which, a masking operation over the effective area of the eye fundus image is performed to obtain the final segmentation of exudates. In the case of blood vessels, the negative of the image filtered with DCT  is first calculated, then a median filter is applied to reduce noise and artifacts, followed by a gamma correction. Again, the Otsu's global threshold method for binarization, next a morphological closing operation is employed, and a masking operation gives the corresponding final segmentation. Illustrative examples taken from a free clinical database are included to demonstrate the capability of the proposed methods.