The emergence of neuromorphic vision sensors (also known as event-based cameras) revolutionized computer vision algorithms in the field of robotics. However, the pixel-independent nature of events and the lack of photometric information raised new challenges. One of the challenges in computer vision, such as line-based SLAM (Simultaneous Localization and Mapping) and visual odometry schemes, is line detection and segmentation. In this paper, we address this problem and provide a fast method to compute line detection by avoiding time-consuming search algorithms and limiting the use of any complex implementation. Our algorithm exploits the geometrical features of the scene and applies simple heuristics to ensure correct line detection and segmentation. As a result, the inherent line detection problems that lead to either heavy computation or false estimation, like the use of search algorithms or wrong line connection, are addressed and tackled. Furthermore, the results are shown to be accurate in different motion scenarios (rotational and translational) and robust against false line segmentation.