How　to　apply　machine　learning　to　retinal　vessel　segmentation　effectively　has　become　a　trend,　however,　choosing　what　kind　of　features　for　the　blood　vessels　is　still　a　problem.　In　this　paper,　the　blood　vessels　of　pixels　are　regarded　as　a　theory　of　binary　classification,　and　a　hybrid　5D　features　for　each　pixel　is　put　forward　to　extract　retinal　blood　vessels　from　the　background　simplely　and　quickly.　The　5D　eigenvector　includes　Contrast　Limited　Adaptive　Histgram　Equalization　(CLAHE),　Gaussian　matched　filter,　Hessian　matrix　transform,　morphological　bottom　hat　transform　and　Bar-selective　Combination　Of　Shifted　Filter　Responses　(B-COSFIRE).　Then　the　fusion　features　are　input　into　the　Support　Vector　Machine　(SVM)　classifier　to　train　a　model　that　is　needed.　The　proposed　method　is　evaluated　on　two　publicly　available　datasets　of　DRIVE　and　STARE,　respectively.　Se,　Sp,　Acc,　Ppv,　Npv,　F1-measure　are　used　to　test　the　proposed　method,　and　average　classification　accuracies　are　0.9573　and　0.9575　on　the　DRIVE　and　STARE　datasets,　respectively.　Performance　results　show　that　the　fusion　method　also　outperform　the　state-of-the-art　method　including　B-COSFIRE　and　other　currently　proposed　fusion　features　method.　©　2017,　Science　Press.　All　right　reserved.