Image processing and analysis play pivotal roles in materials science, manufacturing, and non-destructive testing. By processing computed tomography (CT) images, researchers can derive physical characteristics of an object, such as its boundary, surface, and internal features. Identifying different areas of an image and the edges between them is known as segmentation. Pacific Northwest National Laboratory (PNNL) utilizes CT scans to study objects and materials in an efficient and nondestructive manner. Partnering with PNNL, this Research Experience for Undergraduates project at Embry-Riddle Aeronautical University aims to quantify the uncertainty of edge detection methods applied to CT scans of machined objects. To ensure the reliability and effectiveness of CT, the associated uncertainty in segmentation must be addressed. The first phase in our research applies gradient, statistical, and area-based edge detection methods to a dataset of 176 CT images comprising a single test object provided by PNNL. Evaluation metrics are employed on each edge detection method, and a ground truth reference is generated to determine the accuracy of the algorithms. After selecting the most effective method, frameworks for quantifying error in edge detection are developed, tested, and evaluated. Using the developed error models as a basis, the uncertainty of the CT scan segmentation can be quantified to generate a more robust and productive process to study materials in industry application.