Pectus excavatum is a posterior depression of the sternum and adjacent costal cartilages and is the most common congenital deformity of the anterior chest wall. Its surgical repair can be performed via minimally invasive procedures that involve sternum and cartilage relocation and benefit from adequate surgical planning. In this study, we propose a method to estimate the cartilage regions in thoracic CT scans, which is the first step of statistical modeling of the osseous and cartilaginous structures for the rib cage. The ribs and sternum are first segmented by using interactive region growing and removing the vertebral column with morphological operations. The entire chest wall is also segmented to estimate the skin surface. After the segmentation, surface meshes are generated from the volumetric data and the skeleton of the ribs is extracted using surface contraction method. Then the cartilage surface is approximated via contracting the skin surface to the osseous structure. The ribs’ skeleton is projected to the cartilage surface and the cartilages are estimated using cubic interpolation given the joints with the sternum. The final cartilage regions are formed by the cartilage surface inside the convex hull of the estimated cartilages. The method was validated with the CT scans of two pectus excavatum patients and three healthy subjects. The average distance between the estimated cartilage surface and the ground truth is 2.89 mm. The promising results indicate the effectiveness of cartilage surface estimation using the skin surface.
Down syndrome is the most commonly occurring chromosomal condition; one in every 691 babies in United States is
born with it. Patients with Down syndrome have an increased risk for heart defects, respiratory and hearing problems
and the early detection of the syndrome is fundamental for managing the disease. Clinically, facial appearance is an
important indicator in diagnosing Down syndrome and it paves the way for computer-aided diagnosis based on facial
image analysis. In this study, we propose a novel method to detect Down syndrome using photography for computer-assisted image-based facial dysmorphology. Geometric features based on facial anatomical landmarks, local texture
features based on the Contourlet transform and local binary pattern are investigated to represent facial characteristics.
Then a support vector machine classifier is used to discriminate normal and abnormal cases; accuracy, precision and
recall are used to evaluate the method. The comparison among the geometric, local texture and combined features was
performed using the leave-one-out validation. Our method achieved 97.92% accuracy with high precision and recall for
the combined features; the detection results were higher than using only geometric or texture features. The promising
results indicate that our method has the potential for automated assessment for Down syndrome from simple, noninvasive imaging data.