This research presents a segmentation-based, image processing method to automate the extraction of tidal datum referenced shorelines from airborne light detection and ranging (LIDAR) data. The method first segments a LIDAR digital elevation model (DEM) into a binary image, consisting of land and water pixels, by intersecting the LIDAR DEM and the tidal datum surface. A chain of image processing algorithms, including region grouping and labeling, two passes of image region scanning, a mathematical morphology operation, line tracing, and vectorization, is sequentially applied to the segmented binary image. Our applications to the upper Texas gulf coast show that the method is efficient, accurate, objective, and replicable. Spatially detailed shorelines can be derived from the LIDAR data with minimal human intervention. The method is a substantial technical improvement over standard cross-shore profile and contouring methods. We also examine and quantify the effects of vertical measurement error of the LIDAR system and the uncertainty in tidal datum determination on the shoreline extraction process using the Monte Carlo simulation technique. The Monte Carlo technique allows confidence intervals and summary error statistics to be calculated for each section of the extracted shoreline. Our analysis suggests that the horizontal position of the MHW shoreline derived from the LIDAR data is accurate within 4.5 m at the 95% confidence level.