Abstract: Lightning channel coordinates in digital images are often manually extracted to analyze the development and morphology of lightning channels, but this method is not efficient and its result is often subjective. Therefore, more and more researchers start to investigate approaches to recognize lightning channel information automatically. In general, lightning channel images are complex and diverse because of low contrast, occlusion of clouds, and interference of other environmental factors, so most traditional lightning channel segmentation algorithms do not work well for such lightning images.A new lightning channel segmentation algorithm named LLSR is brought forward based on line support regions. First, Gauss matched filtering and contrast stretching method are applied to enhance the contrast of lightning channels, according to the gray distribution characteristics of cross section of lightning channels. Second, line support regions, which include lightning channels within a minimum enclosing rectangle, are extracted as foreground area by a line segment detection method. In addition, line support regions are expanded in both the main direction and its perpendicular direction. In general, a line support region contains a segment of a lightning channel. Furthermore, it has better contrast between lightning channel and background. Finally, Otsu thresholding method is applied in each line support region to extract lightning channels, because the gray level distribution of each line support region is bimodal. Therefore, lightning channels are segmented from complicated background.A dataset including various types of lightning channel images, are constructed and manually marked to evaluate the proposed algorithm LLSR. Compared with traditional algorithms, global thresholding method (GThres), local thresholding method (LThres), and Canny thresholding method (CThres), the proposed LLSR has higher precision for lightning channel segmentation, and it obtains a better balance between recall rate and false positive rate. Besides, experiment results show that traditional algorithms are not robust enough for all types of lightning images, but the new method demonstrates better generality. LLSR can recognize not only the lightning channels with low contrast but also the lightning channels with complicated background, and the segmentation result is visually consistent with human eyes.