Visual data mining for modeling prior distributions in morphometry. - Machado, A.M.C.
This article presents a novel method for visual data mining based on exploratory factor analysis. Modern imaging modalities provide an overwhelming amount of information that cannot be effectively handled without computerized tools. Data mining techniques aim to discover new knowledge from the collected data and to statistically represent this knowledge in the form of prior distributions that may be used to validate new hypotheses. When applied to morphometric studies, factor analysis is able to minimize data redundancy and reveal subtle or hidden patterns. The characterization of structural shape is performed in a new lower-dimensional basis in which the variables account for the correlation among regions of interest and provide morphological meaning. Data analysis is based on a set of vector variables obtained from image registration. The method is applied to a magnetic resonance imaging (MRI) study of the human corpus callosum and is able to reveal differences in the callosal morphology between male and female samples, based on unsupervised analysis.
Keywords: Callosal morphology; Computerized imaging; Data redundancy; Exploratory factor analysis; Hidden patterns; Human corpus callosum; Image registration; Linear Gaussian model; Magnetic resonance imaging; Morphometry; Prior statistical distribution; Visual data mining