Investigators
Lazarevic Aleksandar
Megalooikonomou Vasileos
Obradovic Zoran
Pokrajac Dragoljub
Problem
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 [1] between distributions estimated through a non-parametric procedure. In addition, supervised neural network models were compared with previous two methods.