Spline based inhomogeneity correction for 11C-PIB PET segmentation using expectation maximization. Academic Article uri icon


  • With the advent of biomarkers such as 11C-PIB and the increase in use of PET, automated methods are required for processing and analyzing datasets from research studies and in clinical settings. A common preprocessing step is the calculation of standardized uptake value ratio (SUVR) for inter-subject normalization. This requires segmented grey matter (GM) for VOI refinement. However 11C-PIB uptake is proportional to amyloid build up leading to inhomogeneities in intensities, especially within GM. Inhomogeneities present a challenge for clustering and pattern classification based approaches to PET segmentation as proposed in current literature. In this paper we modify a MR image segmentation technique based on expectation maximization for 11C-PIB PET segmentation. A priori probability maps of the tissue types are used to initialize and enforce anatomical constraints. We developed a B├ęzier spline based inhomogeneity correction techniques that is embedded in the segmentation algorithm and minimizes inhomogeneity resulting in better segmentations of 11C-PIB PET images. We compare our inhomogeneity with a global polynomial correction technique and validate our approach using co-registered MRI segmentations.


  • Raniga, P
  • Bourgeat, P
  • Villemagne, V
  • O'Keefe, G
  • Rowe, C
  • Ourselin, S

publication date

  • January 1, 2007