Kernel Regularized Bone Surface Reconstruction from Partial Data Using Statistical Shape Model

This paper addresses the problem of surface reconstruction from partial data consisting of digitized landmarks and surface points that are obtained intra-operatively. The surface is derived by deforming a template so that the reconstructed surface matches the digitized points. Two techniques are employed to address such an ill-posed problem. First, a patient-specific template is used, which is computed by optimally fitting a statistical deformable model to partial data. Second, the estimated patient specific template is deformed using a regression technique by carefully designing a regularization term in kernel space. The proposed method is especially useful for accurate and stable surface construction from partial data when only a small sample size of training set is available. It adapts gradually to use more information derived from the statistical shape model when larger data are available. The proposed reconstruction method has been successfully tested on femoral heads, yielding very promising results