When a bullet is fired manufacturing imperfections in the rifling of the barrel of the gun leave unique markings on the fired bullets. Fire examiners utilize these marks to make identifications by matching striation patterns between bullets. In order to meet the increased need for scientific rigor in the field of forensic science, new statistical or machine learning methods have been developed to automate the process of bullet matching using data from 3-dimensional high resolution scans. A key part of extracting data from these scans is the ability to distinguish Groove Engraved Areas(GEAs) of individual bullet lands since these areas do not contain usable striae information. In this paper we present a method for identifying GEAs across entire bullet scans using the low-level computer vision algorithm known as the Hough Transform. The Hough Transform is able to detect the borders of the GEAs and give us the mathematical equations of the borders detected. These equations can then be used to identify GEAs on individual crosscuts of bullet data. When compared to the previously developed method of identifying GEAs, the Hough transform offers a modest improvement in the accuracy with which grooves are identified. Regardless, the appeal of the Hough method lies in its relative simplicity and its ability to draw information from the entirety of a bullet scan. But further examination of the precision of some of the heuristics used to define this process is ultimately needed.