A new algorithm for cortical bone segmentation with its validation and applications to in vivo imaging

Cortical bone is an osseous tissue forming the cortex in our skeleton that supports and protects skeletal functions. Cortical bone segmentation is usually the first step for quantitative cortical bone imaging research. Quality of cortical bone segmentation is one of the most critical factor in determining effectiveness and usefulness of cortical bone measures in a bone imaging study aimed at understanding disease effects, fracture risk and or interventional outcomes. Previous methods primarily focus on local image features and ignore ad therefore fail to utilize larger geometric and topologic contextual knowledge into the segmentation algorithm. Such methods often results in compromised performance under in vivo imaging conditions suffering from low signal to noise ratio and low spatial resolution leaving significant partial volume effects. This thesis presents a new cortical bone segmentation method that utilizes larger contextual and topologic knowledge of distal tibia bone through fuzzy distance transform and connectivity analyses. The input of the method is one threshold and other steps are automatic. An accuracy of 95.1% in terms of percent of volume agreement with gold standard segmentation results and a repeat MD-CT scan intra-class correlation of 98.0% were observed on a cadaveric study. An in vivo study involving sixteen age and body mass index order matched pairs of male and female volunteers has shown that male subjects on average have 16.3% thicker cortex and 4.7% increased porosity as compared to females, and athletes have 3.9% less porosity as compared to control group.