Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features
2014 (English)In: 22nd International Conference on Pattern Recognition (ICPR), 2014, IEEE , 2014, 3274-3279 p.Conference paper (Refereed)
Magnetic resonance (MR) images lack information about radiation transport-a fact which is problematic in applications 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. A whole skull segmentation is achievable in minutes. In a comparison with two other methods, one based on mathematical morphology and the other on deformable registration, the proposed method was found to yield consistently better segmentations.
Place, publisher, year, edition, pages
IEEE , 2014. 3274-3279 p.
, International Conference on Pattern Recognition, ISSN 1051-4651
Bones; Computed tomography; Image segmentation; Magnetic resonance imaging; Positron emission tomography; Support vector machines; Training
IdentifiersURN: urn:nbn:se:liu:diva-113296DOI: 10.1109/ICPR.2014.564ISI: 000359818003068OAI: oai:DiVA.org:liu-113296DiVA: diva2:780820
22nd International Conference on Pattern Recognition (ICPR), 2014, 24-28 August, Stockholm, Sweden