Skull segmentation in MRI by a support vector machine combining local and global features, IEEE Generating pseudo-CT from MR using atlas-based regression 18

Abstract—Magnetic resonance (MR) images lack information about radiation transport—a fact which is problematic in applica-tions such as radiotherapy planning and attenuation correction in combined PET/MR imaging. To remedy this, a crude but common approach is to approximate all tissue properties as equivalent to those of water. We improve upon this using an algorithm that automatically identifies bone tissue in MR. More specifically; we focus on segmenting the skull prior to stereotactic neurosurgery, where it is common that only MR images are available. In the proposed approach, a machine learning algorithm known as a support vector machine is trained on patients for which both a CT and an MR scan are available. As input, a combination of local and global information is used. The latter is needed to distinguish between bone and air as this is not possible based only on the local image intensity