Intelligent Engineering Systems through Artificial Neural Networks
51 Spherical SOFM: Implications for Forensics and Craniofacial Surgery
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The spherical self-organizing feature map (SSOFM) has previously been implemented for data visualization, three-dimensional (3D) freeform surface reconstruction, shape morphing and registration. This paper extends its application to cranial anthropometry. Current cephalometric evaluation techniques involve identifying cranial landmarks and establishing parameters of relative alignment between anatomical structures. These techniques are mostly two-dimensional while available 3D techniques manipulate a dense cloud of points or surface models acquired directly from CT scans. The proposed method adapted a previously developed SSOFM-morphing technique to create low-density tessellations of pre- and post-operative CT scans, both having identical nodal topologies. Cranial landmarks were identified and pre- and postoperative tessellated forms were compared to examine post-surgical cranial growth in craniosynostosis. It further proposes a framework for tissue characterization in forensic facial reconstruction. Both applications use the inherent nodal correspondence established during the shape metamorphosis operation. The paper demonstrates proof-of-concept of the technique and its potential in craniofacial reconstructive surgeries.