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There is an everyday increase of retinal images in different application such as human recognition and diagnosing the eye diseases. Therefore, the need for an automatic method which can recognise the eye parts of retinal images as eye features is unavoidable. This paper offers an automatic and quick morphological-based blood vessel extraction algorithm from the coloured retinal images using Curvelet transform (CT) and principle component analysis (PCA) is proposed. In this algorithm, by estimating the illumination of background and the distribution of contrast in the retinal images, the brightness of images is considerably uniformed. Furthermore, CT is used to enhance the contrast of retinal images by highlighting the edge images in various, scales and directions. We use an improved morphology function introduced with multi-directional structure elements, to extract the blood vessels from retinal images. Connected component analysis and an adaptive filter are used to refine appeared frills with the size of smaller than arterioles in images. The proposed algorithm is evaluated on available images of the DRIVE database and accuracy rate of 94.58% for the blood vessel extraction is obtained. The obtained results show efficiency of the proposed algorithm in comparison with the presented approaches in the literature.