A technique for performing computational analysis of craniosynostosis from CT images is discussed. An image-computing platform assesses craniofacial malformations by using a shape analysis methodology for image processing. The shape of a patient's craniofacial anatomy is assessed in terms of a statistical shape model that incorporates normal shape variability. The model allows for the identification of a closest normal variant to a patient's anatomy rather than comparing the patient's anatomy to an age-specific average shape. Further, an image registration and segmentation method is discussed which enables obtaining a personalized anatomical model for each patient, thereby achieving a precise and systematic characterization of shape abnormality. Visual (qualitative) and quantitative surgical planning, as well as pre-operative and post-operative assessments are achieved. Further, automatic surgical planning for cranial vault reconstruction using tessellation algorithms to establish the relationship between patient's anatomy and the closest normal variant is discussed.
Quantitative assessment of the skull