Assimilating knowledge from neuroimages in schizophrenia diagnostics

The aim of this article is to propose an integrated framework for classifying and describing patterns of disorders from medical images using a combination of image registration, linear discriminant analysis and region-based ontologies. In a first stage of this endeavour we are going to study and evaluate multivariate statistical methodologies to identify the most discriminating hyperplane separating two populations contained in the input data. This step has, as its major goal, the analysis of all the data simultaneously rather than feature by feature. The second stage of this work includes the development of an ontology whose aim is the assimilation and exploration of the knowledge contained in the results of the previous statistical methods. Automated knowledge discovery from images is the key motivation for the methods to be investigated in this research. We argue that such investigation provides a suitable framework for characterising the high complexity of MR images in schizophrenia.