Blood vessel diagnosis and screening procedures help physicians affirm or reduce the presence of vascular disorders. Blood vessel enhancement in medical angio-images is an essential preprocessing step for further handling of blood vessel related issues. Better the enhancement, more accurate is the recognition of abnormal condition pertaining to vessels. In this paper, an adaptive fractional differential filter (AFDF) that adaptively enhances the pixels belonging to vessellike structures is proposed. Initially, Hessian multiscale eigen analysis is used to filter the vessel-like structures from the entire image. It is then refined using thresholding and morphological operations. From the refined results, a piecewise fractional differential function is formed so as to separately enhance weak and strong vessels/background. The fractional differential mask developed based on the Grunwald-Letnikov (G-L) definition uses this fractional function to adjust the fractional order of the vesselness measure. Finally, filtering operation is performed on the image by convolving with this mask. It is found that this algorithm effectively enhances the vessel like structures in both the retinal fundus image and angiogram of cerebral vasculature while preserving the texture and background structures © 2017 Pushpa Publishing House, Allahabad, India.