This paper proposes a novel lane detection method for extracting various lane lines from videos using the concept of directional random walks. Two major components are included in this method, i.e., lane segmentation extraction and edge linking. At first, we define proper structure elements to extract different lane mark features from input frames using a novel morphology-based approach. Then, a novel linking technique is proposed to link all "desired" lane mark features for lane line detection. The technique considers the linking process as a directional random walk which constructs a Markov probability matrix for measuring the direction relationships between lane segments. Then, from the matrix of transition probability, the correct locations of all lane lines can be decided and found from videos. Without defining any mathematical curve models, various road lane shapes and types can be well extracted from road frames even with complicated backgrounds. Experimental results show that the proposed scheme is powerful in lane detection.