A new paradigm to compare a subject to a statistical model. Application to the detection of skull abnormalities
This article deals with the construction of a statistical model which represents the location of clinically relevant regions of the skull. The landmark distribution which is estimated from 3-D CT scans is then modeled using a multivariate Gaussian Markov random field. The main contribution of this paper lies in a new way to characterize what constitutes an anomaly in a subject when it is compared to such a statistical model (which does not need to be a Gaussian Markov random field). Once global abnormality of the subject is detected, local anomalies are searched for by finding the smallest subset of landmarks whose well chosen displacement can render the subject normal according to the statistical model. A proof of concept of the idea is shown through preliminary experiments with a database of 20 subjects used for training and several subjects (including one real subject) used for testing.
multivariate gaussian markov random field subject vs group comparison skull abnormality detection