Uniqueness of Craniometric and Capulometric Landmark Analysis Using Cone Beam Computed Tomography for Individual Identification: A Pilot Study

Abstract

Background: As crimes are committed with highly sophisticated methods and procedures, newer  techniques need to be developed and improved in Forensic as well as crime scene investigations by  applying the benefits of newly emergent and appropriate technological resources. Facial approximations,  developed using 3D sculpting technique, would be enhanced by the availability of modern, well-defined,  consistent, and statistically robust soft tissue data—a situation which is still emerging in craniofacial soft  tissue depth research. In such scenario Cone Beam Computed Tomography offers distinct advantages  over other technology in developing facial approximations for forensic identification utility as it is non invasive and economical. Materials and Methodology: The study has been carried out by measuring the  Facial Soft Tissue Thickness using craniometric and capulometric landmarks that covers anatomical  landmarks of the skull, among 20 subjects, aged between 18 to 30 years. Results: The results of this  study revealed a set of unique Facial Soft Tissue Thickness measurements of the Coastal Karnataka and  North Kerala population that might be useful for facial reconstruction purposes and also to establish  sexual dimorphism in relation to Facial Soft Tissue Thicknesses. The study results also imply that Cone  Beam Computed Tomography can be positively used to record facial soft tissue thickness measurements  for forensic utility. Conclusion: In forensic cases, where identification has been difficult due to non availability of ante-mortem records, efforts will be made for reconstruction of the face of a bare skull  devoid of soft tissue. This study is useful to assess the correlation between craniofacial shape and soft tissue profile from data analyzed by Cone Beam Computed Tomography imaging, so as to determine the  extent to which it might be possible to digitally predict the latter from the former.