A study on model selection from the q-exponential distribution for constructing an organ point distribution model

Abstract

A method is proposed that improves generalization performance of a point distribution model (PDM) of a target organ. Representing the PDM with a directed graphical model (DGM), the proposed method selects an appropriate model for each of the unary terms and of the pairwise terms of the DGM from a q-exponential distribution. The q-exponential distribution has a parameter, q, which controls the tail length, and its representation includes both a Gaussian distribution and a student’s t-distribution: The distribution is identical with a Gaussian distribution when q = 1 and the distribution with a larger value of q has heavier tails. The proposed method selects a value of q for each of the distributions appeared in the DGM based on an Akaike’s information criterion (AIC), which is employed for selecting a model that will minimize the generalization error. The proposed method is applied for the construction of a PDM of the liver and the results show that larger values of q are selected in the posterior region, which contacts with other soft organs.