To
facilitate the process of discovering brain structure-function
associations from image and clinical data and to make retrieval of
similar brain scans possible, we have developed a statistical method
for classification of brain image data based on measures of
dissimilarity between three dimensional probability distributions.
Results
We propose
a method for classifying regions of interest in brain images. The
method is based on computing the Mahalanobis distance between a new
sample and data sets related to each considered class (condition).
The proposed method is compared to an
alternative method for classifying a new subject based on computing
the Kullback-Leibler probabilistic distance between distributions
estimated through a non-parametric procedure. In addition,
supervised neural network models were compared with previous two
methods.
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Investigators