Skeletal and physiological resilience are known to decline with age, resulting in a decreased ability for the body to withstand traumatic insults. Adults 65 years of age and older currently constitute more than 12% of the total population and the elderly population is projected to reach nearly 20% by 2030. The objective of the current study is to quantify age and gender-specific variations in the thoracic skeletal morphology for use in generating a parametric thoracic model for injury prediction. This goal will be accomplished using the image segmentation and registration algorithm developed in this study to collect homologous (or comparable) landmarks from the ribs. A minimum of 10 normal chest CT scans for each gender were collected from a radiological database for the following age groups:​ newborns, 3 month, 6 month, 9 month, 1 year, 3 year, and 6 year olds. Beginning with 10 year olds, a minimum of 10 CT scans for each gender were collected by decade up to age 100. Image segmentation and subsequent image registration of the collected scans was used to collect homologous rib landmarks. A semiautomated method was used to segment each rib and create a mask and three-dimensional (3D) model. Thresholding and region growing operations were applied and manual editing was used to ensure selection of the entire rib and exclusion of surrounding soft tissue. An atlas was created from segmentation of a normal chest CT scan of an average male with over 1,000 landmark points placed on each rib. Each segmented rib is registered to the atlas. Rigid, affine, and non-rigid, nonlinear transformations are used to morph the atlas to the subject rib. The transformation matrices are used to map the landmarks in the atlas coordinate system to the subject-specific coordinate system. Effectively, this allows for collection of homologous rib landmarks across subjects of all ages. Geometric morphometrics, particulary the Procrustes superimposition method can then be used to analyze the landmark data to formulate age and genderspecific shape and size variation functions. Shape and size functions computed from the landmark data can be used to create a scalable finite element model of the thorax that will allow vehicle crashworthiness to be evaluated for all ages and genders and will lead to improvements in restraint systems to better protect children and elderly in a crash.