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Performing segmentation of vasculature with blurry and low contrast boundaries in noisy images is a challenging problem. This paper presents a novel approach to segmenting blood vessels using weighted local variances and an active contour model. In this work, the vessel boundary orientation is estimated locally based on the orientation that minimizes the weighted local variance. Such estimation is less sensitive to noise compared with other common approaches. The edge clearness is measured by the ratio of weighted local variances obtained along different orientations. It is independent of the edge intensity contrast and capable of locating weak boundaries. Integrating the orientation and clearness of edges, an active contour model is employed to align contours that match the contour tangent direction and edge orientation. The proposed method is validated by two synthetic images and two real cases. It is experimentally shown that our method is suitable for dealing with noisy images which consist of structures having blurry and low contrast boundaries, such as blood vessels.