Abstract
In this thesis, we propose a system for vertebrae, spinal cord and nerve segmentation from STIR-MRI images, and estimate the transformation between the segmented 3D vertebra models in CT and STIR-MRI (Short Tau Inversion Recovery - Magnetic Resonance Imaging) by our 3D point-set registration method to combine the 3D spinal models extracted from STIR-MRI and CT. We present a user interactive segmentation approach for the segmentation from 3D images, which is extended from the 2D random walker method and implemented with a slice-section strategy. After the 3D segmentation result is obtained, the 3D spinal cord and vertebra models are reconstructed. Then we apply a deformable registration algorithm based on the Fourier moment matching in conjunction with smoothness constraint to register the pre-built 3D vertebra models in CT and STIR-MRI. Thus, we apply the local affine transformation to integrate the 3D spinal cord and nerve models extracted from STIR-MRI with the 3D vertebra models reconstructed from CT. This is accomplished by applying a linear interpolation method to achieve local affine transformation. In the experimental results, we show the 3D segmentation results of vertebrae and spinal cord from the STIR-MRI images the integrated 3D surface models of the vertebrae, spinal cord and nerves reconstructed from CT and STIR-MRI, and the quantitative evaluation for our registration approach.