Logarithm Odds Maps for Shape Representation
K. Pohl, J. Fisher, M. E. Shenton, R. McCarley, W. Grimson, R. Kikinis, W. Wellsmiccai
Volume 4191, Pages 955-963
October, 2006
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The concept of the Logarithm of the Odds (LogOdds) is frequently used in areas such as artiœóè¡cial neural networks, economics, and biology. Here, we utilize LogOdds for a shape representation that demonstrates desirable prop- erties for medical imaging. For example, the representation encodes the shape of an anatomical structure as well as the variations within that structure. These variations are embedded in a vector space that relates to a probabilistic model. We apply our representation to a voxel based segmentation algorithm. We do so by embedding the manifold of Signed Distance Maps (SDM) into the linear space of LogOdds. The LogOdds variant is superior to the SDM model in an experiment segmenting 20 subjects into subcortical structures. We also use LogOdds in the non-convex interpolation between space condi- tioned distributions. We apply this model to a longitudinal schizophrenia study using quadratic splines. The resulting time-continuous simulation of the schizo- phrenic aging process has a higher accuracy then a model based on convex interpolation.