||Clinical assessment of vasculatures is essential for the detection and treatment of vascular diseases which can be potentially fatal. To facilitate clinical assessment of blood vessels, there is a growing need of developing computer assisted vessel segmentation schemes based on magnetic resonance angiographic (MRA) images. A vast number of approaches have been proposed in the past decade for the segmentation of vascular structures in MRA images. These approaches were devised according to different assumptions on the shape of blood vessels and different underlying prior knowledge about the desired imaging modalities. The development of these approaches aims at delivering more accurate and robust segmentation results. Nonetheless, these approaches face different technical challenges that prohibit them from being widely employed in the clinical environment. The challenges include significant contrast variation of vessel boundaries in MRA images, the excessive computation time required by some algorithms and the complicated geometry of vascular structures. These challenges motivate us to propose three novel edge detection and vascular segmentation methods. In the first proposed method, vessel segmentation is performed grounded on the edge detection responses given by the weighted local variance-based edge detector. This detector is robust against large intensity contrast changes and capable of returning accurate detection responses on low contrast edges. Our second method is an efficient implementation of a well founded vessel detection approach. The proposed efficient implementation is a thousand times faster than the conventional implementation without segmentation performance deterioration. The third method is a curvilinear structure descriptor which is robust against the disturbance induced by closely located objects. Preliminary experimental results show that the proposed methods are very suitable for vascular segmentation in MRA images.