INTRODUCTION: Forensic dental identification relies on the comparison of antemortem and postmortem dental records. 3D dental imaging presents the potential for detailed anatomical features of teeth to be quantified between individuals in automated identification tools. This study introduces a novel segmentation method to simultaneously remove extraneous data from two images reducing processes and time required during 3D dental image comparisons, and tests this against existing approaches to better understand segmentation techniques for forensic purposes. METHODS: Six volunteers had both digital and stone cast full arch dental models created. The casts were scanned and digitized with an intra oral laser scanner, and five different segmentation methods were then applied to all images. Segmented images were compared via a method for aligning 3D images for possible matching (same person) and non-matching (different person) pairings. RESULTS: All segmentation methods removed adequate excess materials to provide consistent repeated outcomes in the comparison process, with the novel segmentation method showing equivalent outcomes with existing methodologies. The findings highlight the importance of understanding the process of segmentation in distinguishing between 3D dental imaging and underscore the potential of 3D imaging technologies in forensic odontology. CONCLUSION: The study demonstrates the efficacy of a new segmentation method in forensic dental identification, offering a faster approach; calling for further validation of these methods within a legal framework.