Blood vessel extraction with optic disc removal in retinal images

Automatic extraction of retinal blood vessels is an important issue for the diagnosis and the treatment of different retinal disorders. Most of the retinal images are of low contrast due to non-uniform illumination during acquisition process. Therefore, vessel extraction from unevenly illuminated retinal background is really a challenging task. To extract the vessels which lie in the optic disc region, the removal of the optic disc is also important. This paper proposes an algorithm for automatic blood vessel extraction and optic disc removal on poorly illuminated retinal images using curvelet transform, morphological operation, matched filtering and fuzzy entropy maximization. Curvelet transform is used to extract the finest details along the vessels since it can represent the lines, the edges, the curvatures, the missing and the imprecise boundary details efficiently. To remove the optic disc, the curvelet based edge enhanced image is first opened by a disk shaped structuring element which is then subtracted from the inverted histogram equalized image. Matched filtering intensifies the blood vessels' response in the enhanced image. The multiple threshold values for the maximum matched filter response that maximize the fuzzy entropy are considered to be the optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to obtain the optimal combination of the fuzzy parameters. Performance evaluated on publicly available DRIVE database demonstrate that the present work outperforms the existing works for various types of vessels extraction and optic disc removal even from poorly illuminated retinal images.