Andrew Litvin

Localized Priors for the Precise Segmentation of Individual Vertebras from CT Volume Data

We present algorithms for the automatic and precise segmentation of individual vertebras in CT Volume data. When a local surface evolution method such as the level set is applied to such a complex structure, global shape priors will not be sufficient to avoid the leakage and local minima problems, particularly if precise object boundary is desired. We propose a prior knowledge base that contains localized priors--a group of high-level features whose detection will augment the surface model and be the key to success. Base on this a set of context blockers are applied to prevent the leakages. Carefully designed initial surface when registered with the data helps avoid the local minimum problem. The results of segmentation well approximate the human delineated object boundaries. We also present the validation result of the segmentation of 150 vertebras.

A correction framework for non-ideal source switching in dual energy CT scanner

Dual energy CT requires two measurements of object attenuation at two different energies. Two measurements can be obtained by switching between two sources operating at two different spectra. Non-ideal source switching causes data mixing which produces image artifacts. We propose a framework to correct data to prevent such image artifacts using a recursive view mixing model. A nonlinear optimization approach estimates the model parameters using data from a calibration procedure based on a symmetry metric. Results from a commercial dual energy CT scanner demonstrate the effectiveness of the proposed approach.